Generalized emissivity inverse problem.
Ming, DengMing; Wen, Tao; Dai, XianXi; Dai, JiXin; Evenson, William E
2002-04-01
Inverse problems have recently drawn considerable attention from the physics community due to of potential widespread applications [K. Chadan and P. C. Sabatier, Inverse Problems in Quantum Scattering Theory, 2nd ed. (Springer Verlag, Berlin, 1989)]. An inverse emissivity problem that determines the emissivity g(nu) from measurements of only the total radiated power J(T) has recently been studied [Tao Wen, DengMing Ming, Xianxi Dai, Jixin Dai, and William E. Evenson, Phys. Rev. E 63, 045601(R) (2001)]. In this paper, a new type of generalized emissivity and transmissivity inverse (GETI) problem is proposed. The present problem differs from our previous work on inverse problems by allowing the unknown (emissivity) function g(nu) to be temperature dependent as well as frequency dependent. Based on published experimental information, we have developed an exact solution formula for this GETI problem. A universal function set suggested for numerical calculation is shown to be robust, making this inversion method practical and convenient for realistic calculations. PMID:12005916
Inverse Problems of Thermoelectricity
NASA Astrophysics Data System (ADS)
Anatychuk, L. I.; Luste, O. J.; Kuz, R. V.; Strutinsky, M. N.
2011-05-01
Classical thermoelectricity is based on the use of the Seebeck and Thomson effects that occur in the near-contact areas between n- and p-type materials. A conceptually different approach to thermoelectric power converter design that is based on the law of thermoelectric induction of currents is also known. The efficiency of this approach has already been demonstrated by its first applications. More than 10 basically new types of thermoelements were discovered with properties that cannot be achieved by thermocouple power converters. Therefore, further development of this concept is of practical interest. This paper provides a classification and theory for solving the inverse problems of thermoelectricity that form the basis for devising new thermoelement types. Computer methods for their solution for anisotropic and inhomogeneous media are elaborated. Regularities related to thermoelectric current excitation in anisotropic and inhomogeneous media are established. The possibility of obtaining eddy currents of a particular configuration through control of the temperature field and material parameters for the creation of new thermo- element types is demonstrated for three-dimensional (3D) models of anisotropic and inhomogeneous media.
Boundary estimation problems arising in thermal tomography
NASA Technical Reports Server (NTRS)
Banks, H. T.; Kojima, Fumio; Winfree, W. P.
1989-01-01
Problems on the identification of two-dimensional spatial domains arising in the detection and characterization of structural flaws in materials are considered. For a thermal diffusion system with external boundary input, observations of the temperature on the surface are used in a output least squares approach. Parameter estimation techniques based on the method of mappings are discussed and approximation schemes are developed based on a finite element Galerkin approach. Theoretical convergence results for computational techniques are given and the results are applied to experimental data for the identification of flaws in the thermal testing of materials.
Inverse problem in hydrogeology
NASA Astrophysics Data System (ADS)
Carrera, Jesús; Alcolea, Andrés; Medina, Agustín; Hidalgo, Juan; Slooten, Luit J.
2005-03-01
The state of the groundwater inverse problem is synthesized. Emphasis is placed on aquifer characterization, where modelers have to deal with conceptual model uncertainty (notably spatial and temporal variability), scale dependence, many types of unknown parameters (transmissivity, recharge, boundary conditions, etc.), nonlinearity, and often low sensitivity of state variables (typically heads and concentrations) to aquifer properties. Because of these difficulties, calibration cannot be separated from the modeling process, as it is sometimes done in other fields. Instead, it should be viewed as one step in the process of understanding aquifer behavior. In fact, it is shown that actual parameter estimation methods do not differ from each other in the essence, though they may differ in the computational details. It is argued that there is ample room for improvement in groundwater inversion: development of user-friendly codes, accommodation of variability through geostatistics, incorporation of geological information and different types of data (temperature, occurrence and concentration of isotopes, age, etc.), proper accounting of uncertainty, etc. Despite this, even with existing codes, automatic calibration facilitates enormously the task of modeling. Therefore, it is contended that its use should become standard practice. L'état du problème inverse des eaux souterraines est synthétisé. L'accent est placé sur la caractérisation de l'aquifère, où les modélisateurs doivent jouer avec l'incertitude des modèles conceptuels (notamment la variabilité spatiale et temporelle), les facteurs d'échelle, plusieurs inconnues sur différents paramètres (transmissivité, recharge, conditions aux limites, etc.), la non linéarité, et souvent la sensibilité de plusieurs variables d'état (charges hydrauliques, concentrations) des propriétés de l'aquifère. A cause de ces difficultés, le calibrage ne peut êtreséparé du processus de modélisation, comme c'est le
Inverse scattering problems with multi-frequencies
NASA Astrophysics Data System (ADS)
Bao, Gang; Li, Peijun; Lin, Junshan; Triki, Faouzi
2015-09-01
This paper is concerned with computational approaches and mathematical analysis for solving inverse scattering problems in the frequency domain. The problems arise in a diverse set of scientific areas with significant industrial, medical, and military applications. In addition to nonlinearity, there are two common difficulties associated with the inverse problems: ill-posedness and limited resolution (diffraction limit). Due to the diffraction limit, for a given frequency, only a low spatial frequency part of the desired parameter can be observed from measurements in the far field. The main idea developed here is that if the reconstruction is restricted to only the observable part, then the inversion will become stable. The challenging task is how to design stable numerical methods for solving these inverse scattering problems inspired by the diffraction limit. Recently, novel recursive linearization based algorithms have been presented in an attempt to answer the above question. These methods require multi-frequency scattering data and proceed via a continuation procedure with respect to the frequency from low to high. The objective of this paper is to give a brief review of these methods, their error estimates, and the related mathematical analysis. More attention is paid to the inverse medium and inverse source problems. Numerical experiments are included to illustrate the effectiveness of these methods.
Optimization and geophysical inverse problems
Barhen, J.; Berryman, J.G.; Borcea, L.; Dennis, J.; de Groot-Hedlin, C.; Gilbert, F.; Gill, P.; Heinkenschloss, M.; Johnson, L.; McEvilly, T.; More, J.; Newman, G.; Oldenburg, D.; Parker, P.; Porto, B.; Sen, M.; Torczon, V.; Vasco, D.; Woodward, N.B.
2000-10-01
A fundamental part of geophysics is to make inferences about the interior of the earth on the basis of data collected at or near the surface of the earth. In almost all cases these measured data are only indirectly related to the properties of the earth that are of interest, so an inverse problem must be solved in order to obtain estimates of the physical properties within the earth. In February of 1999 the U.S. Department of Energy sponsored a workshop that was intended to examine the methods currently being used to solve geophysical inverse problems and to consider what new approaches should be explored in the future. The interdisciplinary area between inverse problems in geophysics and optimization methods in mathematics was specifically targeted as one where an interchange of ideas was likely to be fruitful. Thus about half of the participants were actively involved in solving geophysical inverse problems and about half were actively involved in research on general optimization methods. This report presents some of the topics that were explored at the workshop and the conclusions that were reached. In general, the objective of a geophysical inverse problem is to find an earth model, described by a set of physical parameters, that is consistent with the observational data. It is usually assumed that the forward problem, that of calculating simulated data for an earth model, is well enough understood so that reasonably accurate synthetic data can be generated for an arbitrary model. The inverse problem is then posed as an optimization problem, where the function to be optimized is variously called the objective function, misfit function, or fitness function. The objective function is typically some measure of the difference between observational data and synthetic data calculated for a trial model. However, because of incomplete and inaccurate data, the objective function often incorporates some additional form of regularization, such as a measure of smoothness
Kapteyn series arising in radiation problems
NASA Astrophysics Data System (ADS)
Lerche, I.; Tautz, R. C.
2008-01-01
In discussing radiation from multiple point charges or magnetic dipoles, moving in circles or ellipses, a variety of Kapteyn series of the second kind arises. Some of the series have been known in closed form for a hundred years or more, others appear not to be available to analytic persuasion. This paper shows how 12 such generic series can be developed to produce either closed analytic expressions or integrals that are not analytically tractable. In addition, the method presented here may be of benefit when one has other Kapteyn series of the second kind to consider, thereby providing an additional reason to consider such series anew.
Computationally efficient Bayesian inference for inverse problems.
Marzouk, Youssef M.; Najm, Habib N.; Rahn, Larry A.
2007-10-01
Bayesian statistics provides a foundation for inference from noisy and incomplete data, a natural mechanism for regularization in the form of prior information, and a quantitative assessment of uncertainty in the inferred results. Inverse problems - representing indirect estimation of model parameters, inputs, or structural components - can be fruitfully cast in this framework. Complex and computationally intensive forward models arising in physical applications, however, can render a Bayesian approach prohibitive. This difficulty is compounded by high-dimensional model spaces, as when the unknown is a spatiotemporal field. We present new algorithmic developments for Bayesian inference in this context, showing strong connections with the forward propagation of uncertainty. In particular, we introduce a stochastic spectral formulation that dramatically accelerates the Bayesian solution of inverse problems via rapid evaluation of a surrogate posterior. We also explore dimensionality reduction for the inference of spatiotemporal fields, using truncated spectral representations of Gaussian process priors. These new approaches are demonstrated on scalar transport problems arising in contaminant source inversion and in the inference of inhomogeneous material or transport properties. We also present a Bayesian framework for parameter estimation in stochastic models, where intrinsic stochasticity may be intermingled with observational noise. Evaluation of a likelihood function may not be analytically tractable in these cases, and thus several alternative Markov chain Monte Carlo (MCMC) schemes, operating on the product space of the observations and the parameters, are introduced.
Testing Times: Problems Arising from Misdiagnosis.
ERIC Educational Resources Information Center
Vialle, Wilma; Konza, Deslea
1997-01-01
Three case studies illustrate problems in the identification of gifted students when tests are not used appropriately. The paper concludes that testing must occur within the context of intensive observations of and discussions with the child and family. The importance of all teachers receiving training in gifted education is stressed. (DB)
The continuation inverse problem revisited
NASA Astrophysics Data System (ADS)
Huestis, Stephen P.
1998-06-01
The non-uniqueness of the continuation of a finite collection of harmonic potential field data to a level surface in the source-free region forces its treatment as an inverse problem. A formalism is proposed for the construction of continuation functions which are extremal by various measures. The problem is cast in such a form that the inverse problem solution is the potential function on the lowest horizontal surface above all sources, serving as the boundary function for the Dirichlet problem in the upper half-plane. The desired continuation, at the higher level of interest, must then be in the range of the upward continuation operator acting on this boundary function, rather than being allowed the full freedom of itself being part of a Dirichlet problem boundary function. Extremal solutions minimize non-linear functionals of the continuation function, which are re-expressed as different functionals of the boundary function. A crux of the method is that there is no essential distinction between the upward and downward continuation inverse problems to levels above or below data locations. Casting the optimization as a Lagrange multiplier problem leads to an integral equation for the boundary function, which is readily solved in the Fourier domain for a certain class of functionals. The desired extremal continuation is then given by upward continuation. It is found that for some functionals, application of the Lagrange multiplier theorem requires a further restriction on the set of allowable boundary functions: bandlimitedness is a natural choice for the continuation problem. With this imposition, the theory is developed in detail for semi-norm functionals penalizing departure from a constant potential, in the 2-norm and Sobelev norm senses, and illustrated by application for a small synthetic Deep Tow magnetic field data set.
Uncertainty quantification for ice sheet inverse problems
NASA Astrophysics Data System (ADS)
Petra, N.; Ghattas, O.; Stadler, G.; Zhu, H.
2011-12-01
Modeling the dynamics of polar ice sheets is critical for projections of future sea level rise. Yet, there remain large uncertainties in the basal boundary conditions and in the non-Newtonian constitutive relations employed within ice sheet models. In this presentation, we consider the problem of estimating uncertainty in the solution of (large-scale) ice sheet inverse problems within the framework of Bayesian inference. Computing the general solution of the inverse problem-i.e., the posterior probability density-is intractable with current methods on today's computers, due to the expense of solving the forward model (3D full Stokes flow with nonlinear rheology) and the high dimensionality of the uncertain parameters (which are discretizations of the basal slipperiness field and the Glen's law exponent field). However, under the assumption of Gaussian noise and prior probability densities, and after linearizing the parameter-to-observable map, the posterior density becomes Gaussian, and can therefore be characterized by its mean and covariance. The mean is given by the solution of a nonlinear least squares optimization problem, which is equivalent to a deterministic inverse problem with appropriate interpretation and weighting of the data misfit and regularization terms. To obtain this mean, we solve a deterministic ice sheet inverse problem; here, we infer parameters arising from discretizations of basal slipperiness and rheological exponent fields. For this purpose, we minimize a regularized misfit functional between observed and modeled surface flow velocities. The resulting least squares minimization problem is solved using an adjoint-based inexact Newton method, which uses first and second derivative information. The posterior covariance matrix is given (in the linear-Gaussian case) by the inverse of the Hessian of the least squares cost functional of the deterministic inverse problem. Direct computation of the Hessian matrix is prohibitive, since it would
Estimating uncertainties in complex joint inverse problems
NASA Astrophysics Data System (ADS)
Afonso, Juan Carlos
2016-04-01
Sources of uncertainty affecting geophysical inversions can be classified either as reflective (i.e. the practitioner is aware of her/his ignorance) or non-reflective (i.e. the practitioner does not know that she/he does not know!). Although we should be always conscious of the latter, the former are the ones that, in principle, can be estimated either empirically (by making measurements or collecting data) or subjectively (based on the experience of the researchers). For complex parameter estimation problems in geophysics, subjective estimation of uncertainty is the most common type. In this context, probabilistic (aka Bayesian) methods are commonly claimed to offer a natural and realistic platform from which to estimate model uncertainties. This is because in the Bayesian approach, errors (whatever their nature) can be naturally included as part of the global statistical model, the solution of which represents the actual solution to the inverse problem. However, although we agree that probabilistic inversion methods are the most powerful tool for uncertainty estimation, the common claim that they produce "realistic" or "representative" uncertainties is not always justified. Typically, ALL UNCERTAINTY ESTIMATES ARE MODEL DEPENDENT, and therefore, besides a thorough characterization of experimental uncertainties, particular care must be paid to the uncertainty arising from model errors and input uncertainties. We recall here two quotes by G. Box and M. Gunzburger, respectively, of special significance for inversion practitioners and for this session: "…all models are wrong, but some are useful" and "computational results are believed by no one, except the person who wrote the code". In this presentation I will discuss and present examples of some problems associated with the estimation and quantification of uncertainties in complex multi-observable probabilistic inversions, and how to address them. Although the emphasis will be on sources of uncertainty related
Inverse problem in radionuclide transport
Yu, C.
1988-01-01
The disposal of radioactive waste must comply with the performance objectives set forth in 10 CFR 61 for low-level waste (LLW) and 10 CFR 60 for high-level waste (HLW). To determine probable compliance, the proposed disposal system can be modeled to predict its performance. One of the difficulties encountered in such a study is modeling the migration of radionuclides through a complex geologic medium for the long term. Although many radionuclide transport models exist in the literature, the accuracy of the model prediction is highly dependent on the model parameters used. The problem of using known parameters in a radionuclide transport model to predict radionuclide concentrations is a direct problem (DP); whereas the reverse of DP, i.e., the parameter identification problem of determining model parameters from known radionuclide concentrations, is called the inverse problem (IP). In this study, a procedure to solve IP is tested, using the regression technique. Several nonlinear regression programs are examined, and the best one is recommended. 13 refs., 1 tab.
Minimax approach to inverse problems of geophysics
NASA Astrophysics Data System (ADS)
Balk, P. I.; Dolgal, A. S.; Balk, T. V.; Khristenko, L. A.
2016-03-01
A new approach is suggested for solving the inverse problems that arise in the different fields of applied geophysics (gravity, magnetic, and electrical prospecting, geothermy) and require assessing the spatial region occupied by the anomaly-generating masses in the presence of different types of a priori information. The interpretation which provides the maximum guaranteed proximity of the model field sources to the real perturbing object is treated as the best interpretation. In some fields of science (game theory, economics, operations research), the decision-making principle that lies in minimizing the probable losses which cannot be prevented if the situation develops by the worst-case scenario is referred to as minimax. The minimax criterion of choice is interesting as, instead of being confined to the indirect (and sometimes doubtful) signs of the "optimal" solution, it relies on the actual properties of the information in the results of a particular interpretation. In the hierarchy of the approaches to the solution of the inverse problems of geophysics ordered by the volume and quality of the retrieved information about the sources of the field, the minimax approach should take special place.
Inverse problem for Bremsstrahlung radiation
Voss, K.E.; Fisch, N.J.
1991-10-01
For certain predominantly one-dimensional distribution functions, an analytic inversion has been found which yields the velocity distribution of superthermal electrons given their Bremsstrahlung radiation. 5 refs.
Parabolic Perturbation of a Nonlinear Hyperbolic Problem Arising in Physiology
NASA Astrophysics Data System (ADS)
Colli, P.; Grasselli, M.
We study a transport-diffusion initial value problem where the diffusion codlicient is "small" and the transport coefficient is a time function depending on the solution in a nonlinear and nonlocal way. We show the existence and the uniqueness of a weak solution of this problem. Moreover we discuss its asymptotic behaviour as the diffusion coefficient goes to zero, obtaining a well-posed first-order nonlinear hyperbolic problem. These problems arise from mathematical models of muscle contraction in the framework of the sliding filament theory.
BOOK REVIEW: Inverse Problems. Activities for Undergraduates
NASA Astrophysics Data System (ADS)
Yamamoto, Masahiro
2003-06-01
This book is a valuable introduction to inverse problems. In particular, from the educational point of view, the author addresses the questions of what constitutes an inverse problem and how and why we should study them. Such an approach has been eagerly awaited for a long time. Professor Groetsch, of the University of Cincinnati, is a world-renowned specialist in inverse problems, in particular the theory of regularization. Moreover, he has made a remarkable contribution to educational activities in the field of inverse problems, which was the subject of his previous book (Groetsch C W 1993 Inverse Problems in the Mathematical Sciences (Braunschweig: Vieweg)). For this reason, he is one of the most qualified to write an introductory book on inverse problems. Without question, inverse problems are important, necessary and appear in various aspects. So it is crucial to introduce students to exercises in inverse problems. However, there are not many introductory books which are directly accessible by students in the first two undergraduate years. As a consequence, students often encounter diverse concrete inverse problems before becoming aware of their general principles. The main purpose of this book is to present activities to allow first-year undergraduates to learn inverse theory. To my knowledge, this book is a rare attempt to do this and, in my opinion, a great success. The author emphasizes that it is very important to teach inverse theory in the early years. He writes; `If students consider only the direct problem, they are not looking at the problem from all sides .... The habit of always looking at problems from the direct point of view is intellectually limiting ...' (page 21). The book is very carefully organized so that teachers will be able to use it as a textbook. After an introduction in chapter 1, sucessive chapters deal with inverse problems in precalculus, calculus, differential equations and linear algebra. In order to let one gain some insight
An inverse problem in thermal imaging
NASA Technical Reports Server (NTRS)
Bryan, Kurt; Caudill, Lester F., Jr.
1994-01-01
This paper examines uniqueness and stability results for an inverse problem in thermal imaging. The goal is to identify an unknown boundary of an object by applying a heat flux and measuring the induced temperature on the boundary of the sample. The problem is studied both in the case in which one has data at every point on the boundary of the region and the case in which only finitely many measurements are available. An inversion procedure is developed and used to study the stability of the inverse problem for various experimental configurations.
An inverse acoustic waveguide problem in the time domain
NASA Astrophysics Data System (ADS)
Monk, Peter; Selgas, Virginia
2016-05-01
We consider the problem of locating an obstacle in a waveguide from time domain measurements of causal waves. More precisely, we assume that we are given the scattered field due to point sources placed on a surface located inside the waveguide away from the obstacle, where the scattered field is measured on the same surface. From this multi-static scattering data we wish to determine the position and shape of an obstacle in the waveguide. To deal with this inverse problem, we adapt and analyze the time domain linear sampling method. This involves proving new time domain estimates for the forward problem, as well as analyzing several time domain operators arising in the inversion scheme. We also implement the inversion algorithm and provide numerical results in two-dimensions using synthetic data.
A scatterometry inverse problem in optical mask metrology
NASA Astrophysics Data System (ADS)
Model, R.; Rathsfeld, A.; Gross, H.; Wurm, M.; Bodermann, B.
2008-11-01
We discuss the solution of the inverse problem in scatterometry i.e. the determination of periodic surface structures from light diffraction patterns. With decreasing details of lithography masks, increasing demands on metrology techniques arise. By scatterometry as a non-imaging indirect optical method critical dimensions (CD) like side-wall angles, heights, top and bottom widths are determined. The numerical simulation of diffraction is based on the finite element solution of the Helmholtz equation. The inverse problem seeks to reconstruct the grating geometry from measured diffraction patterns. The inverse operator maps efficiencies of diffracted plane wave modes to the grating parameters. We employ a Newton type iterative method to solve the resulting minimum problem. The reconstruction quality surely depends on the angles of incidence, on the wave lengths and/or the number of propagating scattered wave modes and will be discussed by numerical examples.
NASA Technical Reports Server (NTRS)
Sabatier, P. C.
1972-01-01
The progressive realization of the consequences of nonuniqueness imply an evolution of both the methods and the centers of interest in inverse problems. This evolution is schematically described together with the various mathematical methods used. A comparative description is given of inverse methods in scientific research, with examples taken from mathematics, quantum and classical physics, seismology, transport theory, radiative transfer, electromagnetic scattering, electrocardiology, etc. It is hoped that this paper will pave the way for an interdisciplinary study of inverse problems.
Direct and Inverse problems in Electrocardiography
NASA Astrophysics Data System (ADS)
Boulakia, M.; Fernández, M. A.; Gerbeau, J. F.; Zemzemi, N.
2008-09-01
We present numerical results related to the direct and the inverse problems in electrocardiography. The electrical activity of the heart is described by the bidomain equations. The electrocardiograms (ECGs) recorded in different points on the body surface are obtained by coupling the bidomain equation to a Laplace equation in the torso. The simulated ECGs are quite satisfactory. As regards the inverse problem, our goal is to estimate the parameters of the bidomain-torso model. Here we present some preliminary results of a parameter estimation for the torso model.
Inverse problem of electro-seismic conversion
NASA Astrophysics Data System (ADS)
Chen, Jie; Yang, Yang
2013-11-01
When a porous rock is saturated with an electrolyte, electrical fields are coupled with seismic waves via the electro-seismic conversion. Pride (1994 Phys. Rev. B 50 15678-96) derived the governing models, in which Maxwell equations are coupled with Biot's equations through the electro-kinetic mobility parameter. The inverse problem of the linearized electro-seismic conversion consists in two steps, namely the inversion of Biot's equations and the inversion of Maxwell equations. We analyze the reconstruction of conductivity and electro-kinetic mobility parameter in Maxwell equations with internal measurements, while the internal measurements are provided by the results of the inversion of Biot's equations. We show that knowledge of two internal data based on well-chosen boundary conditions uniquely determines these two parameters. Moreover, a Lipschitz-type stability is proved based on the same sets of well-chosen boundary conditions.
Solving inversion problems with neural networks
NASA Technical Reports Server (NTRS)
Kamgar-Parsi, Behzad; Gualtieri, J. A.
1990-01-01
A class of inverse problems in remote sensing can be characterized by Q = F(x), where F is a nonlinear and noninvertible (or hard to invert) operator, and the objective is to infer the unknowns, x, from the observed quantities, Q. Since the number of observations is usually greater than the number of unknowns, these problems are formulated as optimization problems, which can be solved by a variety of techniques. The feasibility of neural networks for solving such problems is presently investigated. As an example, the problem of finding the atmospheric ozone profile from measured ultraviolet radiances is studied.
MAP estimators and their consistency in Bayesian nonparametric inverse problems
NASA Astrophysics Data System (ADS)
Dashti, M.; Law, K. J. H.; Stuart, A. M.; Voss, J.
2013-09-01
We consider the inverse problem of estimating an unknown function u from noisy measurements y of a known, possibly nonlinear, map {G} applied to u. We adopt a Bayesian approach to the problem and work in a setting where the prior measure is specified as a Gaussian random field μ0. We work under a natural set of conditions on the likelihood which implies the existence of a well-posed posterior measure, μy. Under these conditions, we show that the maximum a posteriori (MAP) estimator is well defined as the minimizer of an Onsager-Machlup functional defined on the Cameron-Martin space of the prior; thus, we link a problem in probability with a problem in the calculus of variations. We then consider the case where the observational noise vanishes and establish a form of Bayesian posterior consistency for the MAP estimator. We also prove a similar result for the case where the observation of {G}(u) can be repeated as many times as desired with independent identically distributed noise. The theory is illustrated with examples from an inverse problem for the Navier-Stokes equation, motivated by problems arising in weather forecasting, and from the theory of conditioned diffusions, motivated by problems arising in molecular dynamics.
A Stochastic Problem Arising in the Storage of Radioactive Waste
Williams, M.M.R.
2004-07-15
Nuclear waste drums can contain a collection of radioactive components of uncertain activity and randomly dispersed in position. This implies that the dose-rate at the surface of different drums in a large assembly of similar drums can have significant variations according to the physical makeup and configuration of the waste components. The present paper addresses this problem by treating the drum, and its waste, as a stochastic medium. It is assumed that the sources in the drum contribute a dose-rate to some external point. The strengths and positions are chosen by random numbers, the dose-rate is calculated and, from several thousand realizations, a probability distribution for the dose-rate is obtained. It is shown that a very close approximation to the dose-rate probability function is the log-normal distribution. This allows some useful statistical indicators, which are of environmental importance, to be calculated with little effort.As an example of a practical situation met in the storage of radioactive waste containers, we study the problem of 'hotspots'. These arise in drums in which most of the activity is concentrated on one radioactive component and hence can lead to the possibility of large surface dose-rates. It is shown how the dose-rate, the variance, and some other statistical indicators depend on the relative activities on the sources. The results highlight the importance of such hotspots and the need to quantify their effect.
Numerical linear algebra for reconstruction inverse problems
NASA Astrophysics Data System (ADS)
Nachaoui, Abdeljalil
2004-01-01
Our goal in this paper is to discuss various issues we have encountered in trying to find and implement efficient solvers for a boundary integral equation (BIE) formulation of an iterative method for solving a reconstruction problem. We survey some methods from numerical linear algebra, which are relevant for the solution of this class of inverse problems. We motivate the use of our constructing algorithm, discuss its implementation and mention the use of preconditioned Krylov methods.
PUBLISHER'S ANNOUNCEMENT: New developments for Inverse Problems
NASA Astrophysics Data System (ADS)
2006-12-01
2006 has proved to be a very successful year for Inverse Problems. After an increase for the fourth successive year, we achieved our highest impact factor to date, 1.541 (Source: 2005 ISI® Journal Citation Report), and the Editorial Board is keen to build on this success by continuing to improve the service we offer to our readers and authors. The Board has observed that Inverse Problems receives very few Letters to the Editor submissions, and that moreover those that we do receive rarely conform to the requirements for Letters to the Editor set out in the journal's editorial policy. The Board has therefore decided to merge the current Letters to the Editor section into our regular Papers section, which will now accommodate all research articles that meet the journal's high quality standards. Any submissions that would previously have been Letters to the Editor are still very welcome as Papers, and can be submitted by e-mail to ip@iop.org or online using our online submissions form at authors.iop.org/submit. Inverse Problems' processing times are already among the fastest in the field—on average, authors receive our decision on their paper in less than three months. Thanks to our easy-to-use online refereeing system, publishing a Paper is now just as fast as publishing a Letter to the Editor, and we are striving to ensure that the journal's high standards are applied consistently to all our Papers, maintaining Inverse Problems' position as the leading journal in the field. Our highly acclaimed Topical Review section will also continue and grow; providing timely insights into the development of all topical fields within Inverse Problems. We have many exciting Topical Reviews currently in preparation for 2007 and will continue to commission articles at the cutting edge of research. We look forward to receiving your contributions and to continuing to provide the best publication service available.
An efficient method for inverse problems
NASA Technical Reports Server (NTRS)
Daripa, Prabir
1987-01-01
A new inverse method for aerodynamic design of subcritical airfoils is presented. The pressure distribution in this method can be prescribed in a natural way, i.e. as a function of arclength of the as yet unknown body. This inverse problem is shown to be mathematically equivalent to solving a single nonlinear boundary value problem subject to known Dirichlet data on the boundary. The solution to this problem determines the airfoil, the free stream Mach number M(sub x) and the upstream flow direction theta(sub x). The existence of a solution for any given pressure distribution is discussed. The method is easy to implement and extremely efficient. We present a series of results for which comparisons are made with the known airfoils.
Urban surface water pollution problems arising from misconnections.
Revitt, D Michael; Ellis, J Bryan
2016-05-01
The impacts of misconnections on the organic and nutrient loadings to surface waters are assessed using specific household appliance data for two urban sub-catchments located in the London metropolitan region and the city of Swansea. Potential loadings of biochemical oxygen demand (BOD), soluble reactive phosphorus (PO4-P) and ammoniacal nitrogen (NH4-N) due to misconnections are calculated for three different scenarios based on the measured daily flows from specific appliances and either measured daily pollutant concentrations or average pollutant concentrations for relevant greywater and black water sources obtained from an extensive review of the literature. Downstream receiving water concentrations, together with the associated uncertainties, are predicted from derived misconnection discharge concentrations and compared to existing freshwater standards for comparable river types. Consideration of dilution ratios indicates that these would need to be of the order of 50-100:1 to maintain high water quality with respect to BOD and NH4-N following typical misconnection discharges but only poor quality for PO4-P is likely to be achievable. The main pollutant loading contributions to misconnections arise from toilets (NH4-N and BOD), kitchen sinks (BOD and PO4-P) washing machines (PO4-P and BOD) and, to a lesser extent, dishwashers (PO4-P). By completely eliminating toilet misconnections and ensuring misconnections from all other appliances do not exceed 2%, the potential pollution problems due to BOD and NH4-N discharges would be alleviated but this would not be the case for PO4-P. In the event of a treatment option being preferred to solve the misconnection problem, it is shown that for an area the size of metropolitan Greater London, a sewage treatment plant with a Population Equivalent value approaching 900,000 would be required to efficiently remove BOD and NH4-N to safely dischargeable levels but such a plant is unlikely to have the capacity to deal
A variational Bayesian method to inverse problems with impulsive noise
NASA Astrophysics Data System (ADS)
Jin, Bangti
2012-01-01
We propose a novel numerical method for solving inverse problems subject to impulsive noises which possibly contain a large number of outliers. The approach is of Bayesian type, and it exploits a heavy-tailed t distribution for data noise to achieve robustness with respect to outliers. A hierarchical model with all hyper-parameters automatically determined from the given data is described. An algorithm of variational type by minimizing the Kullback-Leibler divergence between the true posteriori distribution and a separable approximation is developed. The numerical method is illustrated on several one- and two-dimensional linear and nonlinear inverse problems arising from heat conduction, including estimating boundary temperature, heat flux and heat transfer coefficient. The results show its robustness to outliers and the fast and steady convergence of the algorithm.
Inverse problems biomechanical imaging (Conference Presentation)
NASA Astrophysics Data System (ADS)
Oberai, Assad A.
2016-03-01
It is now well recognized that a host of imaging modalities (a list that includes Ultrasound, MRI, Optical Coherence Tomography, and optical microscopy) can be used to "watch" tissue as it deforms in response to an internal or external excitation. The result is a detailed map of the deformation field in the interior of the tissue. This deformation field can be used in conjunction with a material mechanical response to determine the spatial distribution of material properties of the tissue by solving an inverse problem. Images of material properties thus obtained can be used to quantify the health of the tissue. Recently, they have been used to detect, diagnose and monitor cancerous lesions, detect vulnerable plaque in arteries, diagnose liver cirrhosis, and possibly detect the onset of Alzheimer's disease. In this talk I will describe the mathematical and computational aspects of solving this class of inverse problems, and their applications in biology and medicine. In particular, I will discuss the well-posedness of these problems and quantify the amount of displacement data necessary to obtain a unique property distribution. I will describe an efficient algorithm for solving the resulting inverse problem. I will also describe some recent developments based on Bayesian inference in estimating the variance in the estimates of material properties. I will conclude with the applications of these techniques in diagnosing breast cancer and in characterizing the mechanical properties of cells with sub-cellular resolution.
Inverse scattering problem for quantum graph vertices
Cheon, Taksu; Turek, Ondrej; Exner, Pavel
2011-06-15
We demonstrate how the inverse scattering problem of a quantum star graph can be solved by means of diagonalization of the Hermitian unitary matrix when the vertex coupling is of the scale-invariant (or Fueloep-Tsutsui) form. This enables the construction of quantum graphs with desired properties in a tailor-made fashion. The procedure is illustrated on the example of quantum vertices with equal transmission probabilities.
3D Inverse problem: Seawater intrusions
NASA Astrophysics Data System (ADS)
Steklova, K.; Haber, E.
2013-12-01
Modeling of seawater intrusions (SWI) is challenging as it involves solving the governing equations for variable density flow, multiple time scales and varying boundary conditions. Due to the nonlinearity of the equations and the large aquifer domains, 3D computations are a costly process, particularly when solving the inverse SWI problem. In addition the heads and concentration measurements are difficult to obtain due to mixing, saline wedge location is sensitive to aquifer topography, and there is general uncertainty in initial and boundary conditions and parameters. Some of these complications can be overcome by using indirect geophysical data next to standard groundwater measurements, however, the inverse problem is usually simplified, e.g. by zonation for the parameters based on geological information, steady state substitution of the unknown initial conditions, decoupling the equations or reducing the amount of unknown parameters by covariance analysis. In our work we present a discretization of the flow and solute mass balance equations for variable groundwater (GW) flow. A finite difference scheme is to solve pressure equation and a Semi - Lagrangian method for solute transport equation. In this way we are able to choose an arbitrarily large time step without losing stability up to an accuracy requirement coming from the coupled character of the variable density flow equations. We derive analytical sensitivities of the GW model for parameters related to the porous media properties and also the initial solute distribution. Analytically derived sensitivities reduce the computational cost of inverse problem, but also give insight for maximizing information in collected data. If the geophysical data are available it also enables simultaneous calibration in a coupled hydrogeophysical framework. The 3D inverse problem was tested on artificial time dependent data for pressure and solute content coming from a GW forward model and/or geophysical forward model. Two
Numerical strategies for the solution of inverse problems
NASA Astrophysics Data System (ADS)
Hebber (Haber), Eldad
This thesis deals with the numerical solutions of linear and nonlinear inverse problems. The goal of this thesis is to review and develop new techniques for solving such problems. In so doing, the computations tools for solving inverse problems are comprehensively studied. The thesis can be divided into two parts. In the first part, linear inverse theory is dealt with. Methods to estimate noise and efficiently invert large and full matrixes are reviewed and developed. Emphasis is given to Generalized Cross Validation (GCV) for noise estimation, and to Krylov space methods for efficient methods to invert large systems. This part is summarized by applying and comparing the methods developed on linear inverse problems which arise in gravity and tomography. In the second part of this thesis, extensive use of the linear algebra and the noise estimation methods which were developed in the first part of the thesis is made. A review of the current methods to carry out nonlinear inverse problems is given. A test example is constructed to demonstrate that these methods may fail. Next, a new algorithm for solving nonlinear inverse problems is developed. The algorithm is based on the ability to differentiate between correlated errors which comes from the linearization, and non-correlated noise which comes from the measurement. Based on these two types of noise, a regularization procedure which has two parts is developed. The first part is made of global regulation, to deal with the measurement noise, and the second part is made from a local regularization, to deal with the nonlinearity. The thesis demonstrates that GCV can be used in order to determine the measurement noise, and the Damped Gauss-Newton method can be used in order to deal with the local nonlinear terms. Another aspect of nonlinear inverse theory which is developed in this work concerns approximate sensitivities. A new formulation is suggested for the approximate sensitivities and bounds are calculated using
Discrete Inverse and State Estimation Problems
NASA Astrophysics Data System (ADS)
Wunsch, Carl
2006-06-01
The problems of making inferences about the natural world from noisy observations and imperfect theories occur in almost all scientific disciplines. This book addresses these problems using examples taken from geophysical fluid dynamics. It focuses on discrete formulations, both static and time-varying, known variously as inverse, state estimation or data assimilation problems. Starting with fundamental algebraic and statistical ideas, the book guides the reader through a range of inference tools including the singular value decomposition, Gauss-Markov and minimum variance estimates, Kalman filters and related smoothers, and adjoint (Lagrange multiplier) methods. The final chapters discuss a variety of practical applications to geophysical flow problems. Discrete Inverse and State Estimation Problems is an ideal introduction to the topic for graduate students and researchers in oceanography, meteorology, climate dynamics, and geophysical fluid dynamics. It is also accessible to a wider scientific audience; the only prerequisite is an understanding of linear algebra. Provides a comprehensive introduction to discrete methods of inference from incomplete information Based upon 25 years of practical experience using real data and models Develops sequential and whole-domain analysis methods from simple least-squares Contains many examples and problems, and web-based support through MIT opencourseware
Structural information in the inverse problem
NASA Astrophysics Data System (ADS)
Karaoulis, M.; Larson, T. H.; Ahmed, I.; Revil, A.; Thomason, J.
2013-12-01
Data integration in geophysics provides additional information to elucidate subsurface structure and reduce non-uniqueness of inverted models. There are several strategies for incorporating data integration into numerical models. A traditional approach is to use this information as a-priori knowledge, as an initial model or layer boundary specified on the mesh before the inversion. Although in some cases this approach has proven effective, the information is not directly incorporated into the inverse problem and might be lost in the final model. Another strategy is through joint inversion, where data and models are inverted simultaneous. The data integration comes from the joint operator as structural similarity or petrophysical equations. There are some limitations with this approach. In particular, structural similarity doesn't take into account different sensitivity patterns which differ for different geophysical methods, e.g. in a crosswell configuration electrical resistivity tomography is sensitive close to the borehole region while seismic waves are sensitive towards the center part. Moreover different methods have differing resolution. Therefore, a single joint operator might not be effective in all cases. In this work we demonstrate the use of image-guided inversion, where structural information is taken directly from a high resolution geophysical image (e.g. ground penetrating radar or seismic reflection) or from a geological cross-section. This structural information is introduced into the inverse problem through a weighted smoothing matrix, where it correlates and favors formations related to a specific structural feature and not just uniformly across the entire model. Both sharp and smooth features can be imaged and the recovered models can have a more realistic distribution of values. As an example of the method we use migrated seismic reflection images to extract the structural information and resistivity imaging to recover the resistivity
Direct and inverse problems of infrared tomography.
Sizikov, Valery S; Evseev, Vadim; Fateev, Alexander; Clausen, Sønnik
2016-01-01
The problems of infrared tomography-direct (the modeling of measured functions) and inverse (the reconstruction of gaseous medium parameters)-are considered with a laboratory burner flame as an example of an application. The two measurement modes are used: active (ON) with an external IR source and passive (OFF) without one. Received light intensities on detectors are modeled in the direct problem or measured in the experiment whereas integral equations with respect to the absorption coefficient and Planck function (which yields the temperature profile of the medium) are solved in the inverse problem with (1) modeled and (2) measured received intensities as the input data. An axisymmetric flame and parallel scanning scheme of measurements considered in this work yield singular integral equations that are solved numerically using the generalized quadrature method, spline smoothing, and Tikhonov regularization. A software package in MATLAB has been developed. Two numerical examples-with modeled and real input data-were solved. The proposed methodology avoids the necessity of elaborate determination of the absorption coefficient by direct (point) measurements or calculation using spectroscopic databases (e.g., HITRAN/HITEMP). PMID:26835642
Inverse problem of nonlinear dynamical systems: a constructive approach
Gonzalez-Gascon, F.; Moreno-Insertis, F.; Rodriguez-Camino, E.
1980-08-01
A quite simple and practical method is developed for the construction of two dimensional nonlinear dynamical systems (plane vector fields) possessing an arbitrary number of given limit cycles. The method is applied to the construction of n-dimensional dynamical systems (R/sup n/ vector fields) possessing at least one limit cycle and, under certain circumstances, more than one, or even a numerable infinity. Interesting open problems arise when n is greater than two, or where more than one limit cycle appears. Our constructive algorithm for this type of inverse problem is also applied to the construction of second order differential equations (Newtonian differential equations) possessing a finite or infinite number of invariant speeds. This last problem is relevant for certain aspects of the special theory of relativity.
The inverse problem of the optimal regulator.
NASA Technical Reports Server (NTRS)
Yokoyama, R.; Kinnen, E.
1972-01-01
The inverse problem of the optimal regulator is considered for a general class of multi-input systems with integral-type performance indices. A new phase variable canonical form is shown to be convenient for this analysis. The advantage of the canonical form is to separate the state variables into subvectors of directly controlled, indirectly controlled, and uncontrollable components. Necessary and sufficient conditions for optimized performance indices are given. With the nonlinearities of the system restricted to functions of the directly controlled state variables, additional results are developed about the nonnegative property of optimized loss functions.
EDITORIAL: Inverse Problems' 25th year of publication Inverse Problems' 25th year of publication
NASA Astrophysics Data System (ADS)
2008-01-01
2009 is Inverse Problems' 25th year of publication. In this quarter-century, the journal has established itself as the premier publication venue for inverse problems research. It has matured from its beginnings as a niche journal serving the emerging field of inverse and ill-posed problems to a monthly publication in 2009 covering all aspects of a well-established, vibrant and still-expanding subject. Along with its core readership of pure and applied mathematicians and physicists, Inverse Problems has become widely known across a broad range of researchers in areas such as geophysics, optics, radar, acoustics, communication theory, signal processing and medical imaging, amongst others. The journal's appeal to the inverse problems community and those researchers from the varied fields that encounter such problems can be attributed to our commitment to publishing only the very best papers, and to offering unique services to the community. Besides our regular research papers, which average a remarkably short five months from submission to electronic publication, we regularly publish heavily cited topical review papers and topic-specific special sections, which first appeared in 2004. These highly-downloaded invited articles focus on the latest developments and hot topics in all areas of inverse problems. No other journal in the field offers these features. I am very pleased to take Inverse Problems into its 25th year as Editor-in-Chief. The journal has an impressive tradition of scholarship, established at its inception by the founder and first Editor-in-Chief, Professor Pierre Sabatier. Professor Sabatier envisioned the journal in 1985 as providing a medium for publication of exemplary research in our intrinsically interdisciplinary field. I am glad to say that the support of our authors, readers, referees, Editors-in-Chief, Editorial Boards and Advisory Panels over the years, has resulted in Inverse Problems becoming the top publication in this field, publishing
An inverse problem by boundary element method
Tran-Cong, T.; Nguyen-Thien, T.; Graham, A.L.
1996-02-01
Boundary Element Methods (BEM) have been established as useful and powerful tools in a wide range of engineering applications, e.g. Brebbia et al. In this paper, we report a particular three dimensional implementation of a direct boundary integral equation (BIE) formulation and its application to numerical simulations of practical polymer processing operations. In particular, we will focus on the application of the present boundary element technology to simulate an inverse problem in plastics processing.by extrusion. The task is to design profile extrusion dies for plastics. The problem is highly non-linear due to material viscoelastic behaviours as well as unknown free surface conditions. As an example, the technique is shown to be effective in obtaining the die profiles corresponding to a square viscoelastic extrudate under different processing conditions. To further illustrate the capability of the method, examples of other non-trivial extrudate profiles and processing conditions are also given.
Inverse Variational Problem for Nonstandard Lagrangians
NASA Astrophysics Data System (ADS)
Saha, A.; Talukdar, B.
2014-06-01
In the mathematical physics literature the nonstandard Lagrangians (NSLs) were introduced in an ad hoc fashion rather than being derived from the solution of the inverse problem of variational calculus. We begin with the first integral of the equation of motion and solve the associated inverse problem to obtain some of the existing results for NSLs. In addition, we provide a number of alternative Lagrangian representations. The case studies envisaged by us include (i) the usual modified Emden-type equation, (ii) Emden-type equation with dissipative term quadratic in velocity, (iii) Lotka-Volterra model and (vi) a number of the generic equations for dissipative-like dynamical systems. Our method works for nonstandard Lagrangians corresponding to the usual action integral of mechanical systems but requires modification for those associated with the modified actions like S =∫abe L(x ,x˙ , t) dt and S =∫abL 1 - γ(x ,x˙ , t) dt because in the latter case one cannot construct expressions for the Jacobi integrals.
PREFACE: International Conference on Inverse Problems 2010
NASA Astrophysics Data System (ADS)
Hon, Yiu-Chung; Ling, Leevan
2011-03-01
Following the first International Conference on Inverse Problems - Recent Theoretical Development and Numerical Approaches held at the City University of Hong Kong in 2002, the fifth International Conference was held again at the City University during December 13-17, 2010. This fifth conference was jointly organized by Professor Yiu-Chung Hon (Co-Chair, City University of Hong Kong, HKSAR), Dr Leevan Ling (Co-Chair, Hong Kong Baptist University, HKSAR), Professor Jin Cheng (Fudan University, China), Professor June-Yub Lee (Ewha Womans University, South Korea), Professor Gui-Rong Liu (University of Cincinnati, USA), Professor Jenn-Nan Wang (National Taiwan University, Taiwan), and Professor Masahiro Yamamoto (The University of Tokyo, Japan). It was agreed to alternate holding the conference among the above places (China, Japan, Korea, Taiwan, and Hong Kong) once every two years. The next conference has been scheduled to be held at the Southeast University (Nanjing, China) in 2012. The purpose of this series of conferences is to establish a strong collaborative link among the universities of the Asian-Pacific regions and worldwide leading researchers in inverse problems. The conference addressed both theoretical (mathematics), applied (engineering) and developmental aspects of inverse problems. The conference was intended to nurture Asian-American-European collaborations in the evolving interdisciplinary areas and it was envisioned that the conference would lead to long-term commitments and collaborations among the participating countries and researchers. There was a total of more than 100 participants. A call for the submission of papers was sent out after the conference, and a total of 19 papers were finally accepted for publication in this proceedings. The papers included in the proceedings cover a wide scope, which reflects the current flourishing theoretical and numerical research into inverse problems. Finally, as the co-chairs of the Inverse Problems
A local-order regularization for geophysical inverse problems
NASA Astrophysics Data System (ADS)
Gheymasi, H. Mohammadi; Gholami, A.
2013-11-01
Different types of regularization have been developed to obtain stable solutions to linear inverse problems. Among these, total variation (TV) is known as an edge preserver method, which leads to piecewise constant solutions and has received much attention for solving inverse problems arising in geophysical studies. However, the method shows staircase effects and is not suitable for the models including smooth regions. To overcome the staircase effect, we present a method, which employs a local-order difference operator in the regularization term. This method is performed in two steps: First, we apply a pre-processing step to find the edge locations in the regularized solution using a properly defined minmod limiter, where the edges are determined by a comparison of the solutions obtained using different order regularizations of the TV types. Then, we construct a local-order difference operator based on the information obtained from the pre-processing step about the edge locations, which is subsequently used as a regularization operator in the final sparsity-promoting regularization. Experimental results from the synthetic and real seismic traveltime tomography show that the proposed inversion method is able to retain the smooth regions of the regularized solution, while preserving sharp transitions presented in it.
Bayesian inference tools for inverse problems
NASA Astrophysics Data System (ADS)
Mohammad-Djafari, Ali
2013-08-01
In this paper, first the basics of Bayesian inference with a parametric model of the data is presented. Then, the needed extensions are given when dealing with inverse problems and in particular the linear models such as Deconvolution or image reconstruction in Computed Tomography (CT). The main point to discuss then is the prior modeling of signals and images. A classification of these priors is presented, first in separable and Markovien models and then in simple or hierarchical with hidden variables. For practical applications, we need also to consider the estimation of the hyper parameters. Finally, we see that we have to infer simultaneously on the unknowns, the hidden variables and the hyper parameters. Very often, the expression of this joint posterior law is too complex to be handled directly. Indeed, rarely we can obtain analytical solutions to any point estimators such the Maximum A posteriori (MAP) or Posterior Mean (PM). Three main tools are then can be used: Laplace approximation (LAP), Markov Chain Monte Carlo (MCMC) and Bayesian Variational Approximations (BVA). To illustrate all these aspects, we will consider a deconvolution problem where we know that the input signal is sparse and propose to use a Student-t prior for that. Then, to handle the Bayesian computations with this model, we use the property of Student-t which is modelling it via an infinite mixture of Gaussians, introducing thus hidden variables which are the variances. Then, the expression of the joint posterior of the input signal samples, the hidden variables (which are here the inverse variances of those samples) and the hyper-parameters of the problem (for example the variance of the noise) is given. From this point, we will present the joint maximization by alternate optimization and the three possible approximation methods. Finally, the proposed methodology is applied in different applications such as mass spectrometry, spectrum estimation of quasi periodic biological signals and
An Inverse Problem of Derivative Security Pricing
NASA Astrophysics Data System (ADS)
Zhang, Guanquan; Li, Peijun
2003-04-01
Suppose that interest rate is governed by a stochastic differential equation, a partial differential equation for the price of bond can be derived in a similar way to the derivation of the Black-Scholes equation. Valuation of bond with implied function in the equation, which is called the risk market price of interest rate, is known as the model of bond pricing. An inverse problem of bond pricing is to determined the risk market price of interest rate implied by current prices of bonds with different expirations. In this paper, numerical algorithm to solve this system is constructed and some numerical experiments are performed. The numerical results show that the algorithm is quite efficient and robust.
Stochastic inverse problems: Models and metrics
Sabbagh, Elias H.; Sabbagh, Harold A.; Murphy, R. Kim; Aldrin, John C.; Annis, Charles; Knopp, Jeremy S.
2015-03-31
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.
Stability analysis of the inverse transmembrane potential problem in electrocardiography
NASA Astrophysics Data System (ADS)
Burger, Martin; Mardal, Kent-André; Nielsen, Bjørn Fredrik
2010-10-01
In this paper we study some mathematical properties of an inverse problem arising in connection with electrocardiograms (ECGs). More specifically, we analyze the possibility for recovering the transmembrane potential in the heart from ECG recordings, a challenge currently investigated by a growing number of groups. Our approach is based on the bidomain model for the electrical activity in the myocardium, and leads to a parameter identification problem for elliptic partial differential equations (PDEs). It turns out that this challenge can be split into two subproblems: the task of recovering the potential at the heart surface from body surface recordings; the problem of computing the transmembrane potential inside the heart from the potential determined at the heart surface. Problem (1), which can be formulated as the Cauchy problem for an elliptic PDE, has been extensively studied and is well known to be severely ill-posed. The main purpose of this paper is to prove that problem (2) is stable and well posed if a suitable prior is available. Moreover, our theoretical findings are illuminated by a series of numerical experiments. Finally, we discuss some aspects of uniqueness related to the anisotropy in the heart.
The relativistic inverse stellar structure problem
Lindblom, Lee
2014-01-14
The observable macroscopic properties of relativistic stars (whose equations of state are known) can be predicted by solving the stellar structure equations that follow from Einstein’s equation. For neutron stars, however, our knowledge of the equation of state is poor, so the direct stellar structure problem can not be solved without modeling the highest density part of the equation of state in some way. This talk will describe recent work on developing a model independent approach to determining the high-density neutron-star equation of state by solving an inverse stellar structure problem. This method uses the fact that Einstein’s equation provides a deterministic relationship between the equation of state and the macroscopic observables of the stars which are composed of that material. This talk illustrates how this method will be able to determine the high-density part of the neutron-star equation of state with few percent accuracy when high quality measurements of the masses and radii of just two or three neutron stars become available. This talk will also show that this method can be used with measurements of other macroscopic observables, like the masses and tidal deformabilities, which can (in principle) be measured by gravitational wave observations of binary neutron-star mergers.
Direct and Inverse Problems in Statistical Wavefields
Wolf, Emil
2002-09-01
In this report account is presented of research carried out during the period September 1, 1999-August 31, 2002 under the sponsorship of the Department of Energy, grant DE-FG02-90ER14119. The research covered several areas of modern optical physics, particularly propagation of partially coherent light and its interaction with deterministic and with random media, spectroscopy with partially coherent light, polarization properties of statistical wave fields, effects of moving diffusers on coherence and on the spectra of light transmitted and scattered by them, reciprocity inequalities involving spatial and angular correlations of partially coherent beams, spreading of partially coherent beams in-random media, inverse source problems, computed and diffraction tomography and partially coherent solitons. We have discovered a new phenomenon in an emerging field of physical optics, known as singular optics; specifically we found that the spectrum of light changes drastically in the neighborhood of points where the intensity has zero value and where, consequently, the phase becomes singular, We noted some potential applications of this phenomenon. The results of our investigations were reported in 39 publications. They are listed on pages 3 to 5. Summaries of these publications are given on pages 6-13. Scientists who have participated in this research are listed on page 14.
PREFACE: International Conference on Inverse Problems 2010
NASA Astrophysics Data System (ADS)
Hon, Yiu-Chung; Ling, Leevan
2011-03-01
Following the first International Conference on Inverse Problems - Recent Theoretical Development and Numerical Approaches held at the City University of Hong Kong in 2002, the fifth International Conference was held again at the City University during December 13-17, 2010. This fifth conference was jointly organized by Professor Yiu-Chung Hon (Co-Chair, City University of Hong Kong, HKSAR), Dr Leevan Ling (Co-Chair, Hong Kong Baptist University, HKSAR), Professor Jin Cheng (Fudan University, China), Professor June-Yub Lee (Ewha Womans University, South Korea), Professor Gui-Rong Liu (University of Cincinnati, USA), Professor Jenn-Nan Wang (National Taiwan University, Taiwan), and Professor Masahiro Yamamoto (The University of Tokyo, Japan). It was agreed to alternate holding the conference among the above places (China, Japan, Korea, Taiwan, and Hong Kong) once every two years. The next conference has been scheduled to be held at the Southeast University (Nanjing, China) in 2012. The purpose of this series of conferences is to establish a strong collaborative link among the universities of the Asian-Pacific regions and worldwide leading researchers in inverse problems. The conference addressed both theoretical (mathematics), applied (engineering) and developmental aspects of inverse problems. The conference was intended to nurture Asian-American-European collaborations in the evolving interdisciplinary areas and it was envisioned that the conference would lead to long-term commitments and collaborations among the participating countries and researchers. There was a total of more than 100 participants. A call for the submission of papers was sent out after the conference, and a total of 19 papers were finally accepted for publication in this proceedings. The papers included in the proceedings cover a wide scope, which reflects the current flourishing theoretical and numerical research into inverse problems. Finally, as the co-chairs of the Inverse Problems
A Riemann–Hilbert approach to the inverse problem for the Stark operator on the line
NASA Astrophysics Data System (ADS)
Its, A.; Sukhanov, V.
2016-05-01
The paper is concerned with the inverse scattering problem for the Stark operator on the line with a potential from the Schwartz class. In our study of the inverse problem, we use the Riemann–Hilbert formalism. This allows us to overcome the principal technical difficulties which arise in the more traditional approaches based on the Gel’fand–Levitan–Marchenko equations, and indeed solve the problem. We also produce a complete description of the relevant scattering data (which have not been obtained in the previous works on the Stark operator) and establish the bijection between the Schwartz class potentials and the scattering data.
Inverse Modelling Problems in Linear Algebra Undergraduate Courses
ERIC Educational Resources Information Center
Martinez-Luaces, Victor E.
2013-01-01
This paper will offer an analysis from a theoretical point of view of mathematical modelling, applications and inverse problems of both causation and specification types. Inverse modelling problems give the opportunity to establish connections between theory and practice and to show this fact, a simple linear algebra example in two different…
A Forward Glimpse into Inverse Problems through a Geology Example
ERIC Educational Resources Information Center
Winkel, Brian J.
2012-01-01
This paper describes a forward approach to an inverse problem related to detecting the nature of geological substrata which makes use of optimization techniques in a multivariable calculus setting. The true nature of the related inverse problem is highlighted. (Contains 2 figures.)
Numerical boundary condition procedure for the transonic axisymmetric inverse problem
NASA Technical Reports Server (NTRS)
Shankar, V.
1981-01-01
Two types of boundary condition procedures for the axisymmetric inverse problem are described. One is a Neumann type boundary condition (analogous to the analysis problem) and the other is a Dirichlet type boundary conditon, both requiring special treatments to make the inverse scheme numerically stable. The dummy point concept is utilized in implementing both. Results indicate the Dirichlet type inverse boundary condition is more robust and conceptually simpler to implement than the Neumann type procedure. A few results demonstrating the powerful capability of the newly developed inverse method that can handle both shocked as well as shockless body design are included.
Solving ill-posed magnetic inverse problem using a Parameterized Trust-Region Sub-problem
NASA Astrophysics Data System (ADS)
Abdelazeem, Maha Mohamed
2013-06-01
The aim of this paper is to find a plausible and stable solution for the inverse geophysical magnetic problem. Most of the inverse problems in geophysics are considered as ill-posed ones. This is not necessarily due to complex geological situations, but it may arise because of ill-conditioned kernel matrix. To deal with such ill-conditioned matrix, one may truncate the most ill part as in truncated singular value decomposition method (TSVD). In such a method, the question will be where to truncate? In this paper, for comparison, we first try the adaptive pruning algorithm for the discrete L-curve criterion to estimate the regularization parameter for TSVD method. Linear constraints have been added to the ill-conditioned matrix. The same problem is then solved using a global optimizing and regularizing technique based on Parameterized Trust Region Sub-problem (PTRS). The criteria of such technique are to choose a trusted region of the solutions and then to find the satisfying minimum to the objective function. The ambiguity is controlled mainly by proper choosing the trust region. To overcome the natural decay in kernel with depth, a specific depth weighting function is used. A Matlab-based inversion code is implemented and tested on two synthetic total magnetic fields contaminated with different levels of noise to simulate natural fields. The results of PTRS are compared with those of TSVD with adaptive pruning L-curve. Such a comparison proves the high stability of the PTRS method in dealing with potential field problems. The capability of such technique has been further tested by applying it to real data from Saudi Arabia and Italy.
An inverse problem for a class of conditional probability measure-dependent evolution equations
NASA Astrophysics Data System (ADS)
Mirzaev, Inom; Byrne, Erin C.; Bortz, David M.
2016-09-01
We investigate the inverse problem of identifying a conditional probability measure in measure-dependent evolution equations arising in size-structured population modeling. We formulate the inverse problem as a least squares problem for the probability measure estimation. Using the Prohorov metric framework, we prove existence and consistency of the least squares estimates and outline a discretization scheme for approximating a conditional probability measure. For this scheme, we prove general method stability. The work is motivated by partial differential equation models of flocculation for which the shape of the post-fragmentation conditional probability measure greatly impacts the solution dynamics. To illustrate our methodology, we apply the theory to a particular PDE model that arises in the study of population dynamics for flocculating bacterial aggregates in suspension, and provide numerical evidence for the utility of the approach.
Uniqueness in inverse boundary value problems for fractional diffusion equations
NASA Astrophysics Data System (ADS)
Li, Zhiyuan; Imanuvilov, Oleg Yu; Yamamoto, Masahiro
2016-01-01
We consider an inverse boundary value problem for diffusion equations with multiple fractional time derivatives. We prove the uniqueness in determining the number of fractional time-derivative terms, the orders of the derivatives and spatially varying coefficients.
Inverse kinematics problem in robotics using neural networks
NASA Technical Reports Server (NTRS)
Choi, Benjamin B.; Lawrence, Charles
1992-01-01
In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.
Inverse radiation problem in axisymmetric cylindrical scattering media
NASA Astrophysics Data System (ADS)
Menguc, M. P.; Manickavasagam, S.
1993-09-01
A semianalytical technique has been developed to solve the inverse radiation problem in absorbing and scattering cylindrical media. The radiative properties in the medium are allowed to vary radially. Isotropic, linearly anisotropic, and Rayleigh scattering phase functions are considered, and both the first- and second-order scattering of radiation are accounted for in the analysis. The angular radiosity distribution obtained from the solution of the forward problem is employed as input to the inverse analysis. A numerical inversion scheme is followed to determine the profiles of extinction coefficient and the single-scattering albedo. For an anisotropically scattering medium, the asymmetry factor is also recovered. It is shown that the method is simple and accurate, even though the inversion is limited to three- or four-layer media. This inversion procedure can easily be used in experiments to determine the effective radiative property distributions in cylindrical systems.
Gehre, Matthias; Jin, Bangti
2014-02-15
In this paper, we study a fast approximate inference method based on expectation propagation for exploring the posterior probability distribution arising from the Bayesian formulation of nonlinear inverse problems. It is capable of efficiently delivering reliable estimates of the posterior mean and covariance, thereby providing an inverse solution together with quantified uncertainties. Some theoretical properties of the iterative algorithm are discussed, and the efficient implementation for an important class of problems of projection type is described. The method is illustrated with one typical nonlinear inverse problem, electrical impedance tomography with complete electrode model, under sparsity constraints. Numerical results for real experimental data are presented, and compared with that by Markov chain Monte Carlo. The results indicate that the method is accurate and computationally very efficient.
The metric-restricted inverse design problem
NASA Astrophysics Data System (ADS)
Acharya, Amit; Lewicka, Marta; Pakzad, Mohammad Reza
2016-06-01
We study a class of design problems in solid mechanics, leading to a variation on the classical question of equi-dimensional embeddability of Riemannian manifolds. In this general new context, we derive a necessary and sufficient existence condition, given through a system of total differential equations, and discuss its integrability. In the classical context, the same approach yields conditions of immersibility of a given metric in terms of the Riemann curvature tensor. In the present situation, the equations do not close in a straightforward manner, and successive differentiation of the compatibility conditions leads to a new algebraic description of integrability. We also recast the problem in a variational setting and analyze the infimum of the appropriate incompatibility energy, resembling the non-Euclidean elasticity. We then derive a Γ -convergence result for dimension reduction from 3d to 2d in the Kirchhoff energy scaling regime.
Solving inverse problems of identification type by optimal control methods
Lenhart, S.; Protopopescu, V.; Yong, J.
1997-05-01
Inverse problems of identification type for nonlinear equations are considered within the framework of optimal control theory. The rigorous solution of any particular problem depends on the functional setting, type of equation, and unknown quantity (or quantities) to be determined. Here we present only the general articulations of the formalism. Compared to classical regularization methods (e.g. Tikhonov coupled with optimization schemes), our approach presents several advantages, namely: (i) a systematic procedure to solve inverse problems of identification type; (ii) an explicit expression for the approximations of the solution; and (iii) a convenient numerical solution of these approximations. {copyright} {ital 1997 American Institute of Physics.}
Solving inverse problems of identification type by optimal control methods
Lenhart, S.; Protopopescu, V.; Jiongmin Yong
1997-06-01
Inverse problems of identification type for nonlinear equations are considered within the framework of optimal control theory. The rigorous solution of any particular problem depends on the functional setting, type of equation, and unknown quantity (or quantities) to be determined. Here the authors present only the general articulations of the formalism. Compared to classical regularization methods (e.g. Tikhonov coupled with optimization schemes), their approach presents several advantages, namely: (i) a systematic procedure to solve inverse problems of identification type; (ii) an explicit expression for the approximations of the solution; and (iii) a convenient numerical solution of these approximations.
Minimax theory for a class of nonlinear statistical inverse problems
NASA Astrophysics Data System (ADS)
Ray, Kolyan; Schmidt-Hieber, Johannes
2016-06-01
We study a class of statistical inverse problems with nonlinear pointwise operators motivated by concrete statistical applications. A two-step procedure is proposed, where the first step smoothes the data and inverts the nonlinearity. This reduces the initial nonlinear problem to a linear inverse problem with deterministic noise, which is then solved in a second step. The noise reduction step is based on wavelet thresholding and is shown to be minimax optimal (up to logarithmic factors) in a pointwise function-dependent sense. Our analysis is based on a modified notion of Hölder smoothness scales that are natural in this setting.
Finite element neural networks for electromagnetic inverse problems
NASA Astrophysics Data System (ADS)
Ramuhalli, P.; Udpa, L.; Udpa, S.
2002-05-01
Iterative approaches using numerical forward models are commonly used for solving inverse problems in nondestructive evaluation. The drawbacks of these approaches include their high computational cost and the difficulty in computing gradients for updating defect profiles. This paper proposes a finite element neural network (FENN) that embeds finite element models into a neural network format. This approach enables fast and accurate solution of the forward problem. The FENN can then be used as the forward model in an iterative approach to solve the inverse problem. Gradient-based optimization methods are easily applied since the FENN provides an explicit functional mapping between the defect profile and the measured signal. Results of applying the FENN to several simple electromagnetic forward and inverse problems are presented.
Correct averaging in transmission radiography: Analysis of the inverse problem
NASA Astrophysics Data System (ADS)
Wagner, Michael; Hampel, Uwe; Bieberle, Martina
2016-05-01
Transmission radiometry is frequently used in industrial measurement processes as a means to assess the thickness or composition of a material. A common problem encountered in such applications is the so-called dynamic bias error, which results from averaging beam intensities over time while the material distribution changes. We recently reported on a method to overcome the associated measurement error by solving an inverse problem, which in principle restores the exact average attenuation by considering the Poisson statistics of the underlying particle or photon emission process. In this paper we present a detailed analysis of the inverse problem and its optimal regularized numerical solution. As a result we derive an optimal parameter configuration for the inverse problem.
Inverse Problem in Nondestructive Testing Using Arrayed Eddy Current Sensors
Zaoui, Abdelhalim; Menana, Hocine; Feliachi, Mouloud; Berthiau, Gérard
2010-01-01
A fast crack profile reconstitution model in nondestructive testing is developed using an arrayed eddy current sensor. The inverse problem is based on an iterative solving of the direct problem using genetic algorithms. In the direct problem, assuming a current excitation, the incident field produced by all the coils of the arrayed sensor is obtained by the translation and superposition of the 2D axisymmetric finite element results obtained for one coil; the impedance variation of each coil, due to the crack, is obtained by the reciprocity principle involving the dyadic Green’s function. For the inverse problem, the surface of the crack is subdivided into rectangular cells, and the objective function is expressed only in terms of the depth of each cell. The evaluation of the dyadic Green’s function matrix is made independently of the iterative procedure, making the inversion very fast. PMID:22163680
Inverse problem in nondestructive testing using arrayed eddy current sensors.
Zaoui, Abdelhalim; Menana, Hocine; Feliachi, Mouloud; Berthiau, Gérard
2010-01-01
A fast crack profile reconstitution model in nondestructive testing is developed using an arrayed eddy current sensor. The inverse problem is based on an iterative solving of the direct problem using genetic algorithms. In the direct problem, assuming a current excitation, the incident field produced by all the coils of the arrayed sensor is obtained by the translation and superposition of the 2D axisymmetric finite element results obtained for one coil; the impedance variation of each coil, due to the crack, is obtained by the reciprocity principle involving the dyadic Green's function. For the inverse problem, the surface of the crack is subdivided into rectangular cells, and the objective function is expressed only in terms of the depth of each cell. The evaluation of the dyadic Green's function matrix is made independently of the iterative procedure, making the inversion very fast. PMID:22163680
On the Inverse Problems of Nonlinear Acoustics and Acoustic Turbulence
NASA Astrophysics Data System (ADS)
Gurbatov, S. N.; Rudenko, O. V.
2015-12-01
We consider the problem of retrieval of the radiated acoustic signal parameters from the measured wave field in some cross section of the nonlinear medium. The possibilities of solving regular and statistical inverse problems are discussed on the basis of the solution of the Burgers equation for zero and infinitesimal viscosities.
Analytic semigroups: Applications to inverse problems for flexible structures
NASA Technical Reports Server (NTRS)
Banks, H. T.; Rebnord, D. A.
1990-01-01
Convergence and stability results for least squares inverse problems involving systems described by analytic semigroups are presented. The practical importance of these results is demonstrated by application to several examples from problems of estimation of material parameters in flexible structures using accelerometer data.
Variational structure of inverse problems in wave propagation and vibration
Berryman, J.G.
1995-03-01
Practical algorithms for solving realistic inverse problems may often be viewed as problems in nonlinear programming with the data serving as constraints. Such problems are most easily analyzed when it is possible to segment the solution space into regions that are feasible (satisfying all the known constraints) and infeasible (violating some of the constraints). Then, if the feasible set is convex or at least compact, the solution to the problem will normally lie on the boundary of the feasible set. A nonlinear program may seek the solution by systematically exploring the boundary while satisfying progressively more constraints. Examples of inverse problems in wave propagation (traveltime tomography) and vibration (modal analysis) will be presented to illustrate how the variational structure of these problems may be used to create nonlinear programs using implicit variational constraints.
From inverse problems in mathematical physiology to quantitative differential diagnoses.
Zenker, Sven; Rubin, Jonathan; Clermont, Gilles
2007-11-01
The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses
Inverse problems of ultrasound tomography in models with attenuation
NASA Astrophysics Data System (ADS)
Goncharsky, Alexander V.; Romanov, Sergey Y.
2014-04-01
We develop efficient methods for solving inverse problems of ultrasound tomography in models with attenuation. We treat the inverse problem as a coefficient inverse problem for unknown coordinate-dependent functions that characterize both the speed cross section and the coefficients of the wave equation describing attenuation in the diagnosed region. We derive exact formulas for the gradient of the residual functional in models with attenuation, and develop efficient algorithms for minimizing the gradient of the residual by solving the conjugate problem. These algorithms are easy to parallelize when implemented on supercomputers, allowing the computation time to be reduced by a factor of several hundred compared to a PC. The numerical analysis of model problems shows that it is possible to reconstruct not only the speed cross section, but also the properties of the attenuating medium. We investigate the choice of the initial approximation for iterative algorithms used to solve inverse problems. The algorithms considered are primarily meant for the development of ultrasound tomographs for differential diagnosis of breast cancer.
PREFACE: Inverse Problems in Applied Sciences—towards breakthrough
NASA Astrophysics Data System (ADS)
Cheng, Jin; Iso, Yuusuke; Nakamura, Gen; Yamamoto, Masahiro
2007-06-01
These are the proceedings of the international conference `Inverse Problems in Applied Sciences—towards breakthrough' which was held at Hokkaido University, Sapporo, Japan on 3-7 July 2006 (http://coe.math.sci.hokudai.ac.jp/sympo/inverse/). There were 88 presentations and more than 100 participants, and we are proud to say that the conference was very successful. Nowadays, many new activities on inverse problems are flourishing at many centers of research around the world, and the conference has successfully gathered a world-wide variety of researchers. We believe that this volume contains not only main papers, but also conveys the general status of current research into inverse problems. This conference was the third biennial international conference on inverse problems, the core of which is the Pan-Pacific Asian area. The purpose of this series of conferences is to establish and develop constant international collaboration, especially among the Pan-Pacific Asian countries, and to lead the organization of activities concerning inverse problems centered in East Asia. The first conference was held at City University of Hong Kong in January 2002 and the second was held at Fudan University in June 2004. Following the preceding two successes, the third conference was organized in order to extend the scope of activities and build useful bridges to the next conference in Seoul in 2008. Therefore this third biennial conference was intended not only to establish collaboration and links between researchers in Asia and leading researchers worldwide in inverse problems but also to nurture interdisciplinary collaboration in theoretical fields such as mathematics, applied fields and evolving aspects of inverse problems. For these purposes, we organized tutorial lectures, serial lectures and a panel discussion as well as conference research presentations. This volume contains three lecture notes from the tutorial and serial lectures, and 22 papers. Especially at this
Improved TV-CS Approaches for Inverse Scattering Problem
Bevacqua, M. T.; Di Donato, L.
2015-01-01
Total Variation and Compressive Sensing (TV-CS) techniques represent a very attractive approach to inverse scattering problems. In fact, if the unknown is piecewise constant and so has a sparse gradient, TV-CS approaches allow us to achieve optimal reconstructions, reducing considerably the number of measurements and enforcing the sparsity on the gradient of the sought unknowns. In this paper, we introduce two different techniques based on TV-CS that exploit in a different manner the concept of gradient in order to improve the solution of the inverse scattering problems obtained by TV-CS approach. Numerical examples are addressed to show the effectiveness of the method. PMID:26495420
History and evolution of methods for solving the inverse problem.
van Oosterom, A
1991-10-01
This article serves as an introduction to the other articles in this issue devoted to the problem of the localization of neural generators. Elements of the theory of electric volume conduction are briefly introduced, as far as these apply to the interpretation of observed scalp potentials. First, some basic methods for display of the different aspects of the spatiotemporal information are described. Next, the most prominent source and volume conductor models that have been postulated for the involved forward problem are summarized. The problems of source identification and source localization, known as the inverse problem, are then formulated in terms of a parameter estimation procedure. The importance of introducing a priori information in the inverse problem, aimed at stabilizing (regularizing) the obtained solution, is emphasized. Methods for imposing such constraints are briefly outlined. PMID:1761703
Solving probabilistic inverse problems rapidly with prior samples
NASA Astrophysics Data System (ADS)
Käufl, Paul; Valentine, Andrew P.; de Wit, Ralph W.; Trampert, Jeannot
2016-06-01
Owing to the increasing availability of computational resources, in recent years the probabilistic solution of non-linear, geophysical inverse problems by means of sampling methods has become increasingly feasible. Nevertheless, we still face situations in which a Monte Carlo approach is not practical. This is particularly true in cases where the evaluation of the forward problem is computationally intensive or where inversions have to be carried out repeatedly or in a timely manner, as in natural hazards monitoring tasks such as earthquake early warning. Here, we present an alternative to Monte Carlo sampling, in which inferences are entirely based on a set of prior samples-that is, samples that have been obtained independent of a particular observed datum. This has the advantage that the computationally expensive sampling stage becomes separated from the inversion stage, and the set of prior samples-once obtained-can be reused for repeated evaluations of the inverse mapping without additional computational effort. This property is useful if the problem is such that repeated inversions of independent data have to be carried out. We formulate the inverse problem in a Bayesian framework and present a practical way to make posterior inferences based on a set of prior samples. We compare the prior sampling based approach to a Markov Chain Monte Carlo approach that samples from the posterior probability distribution. We show results for both a toy example, and a realistic seismological source parameter estimation problem. We find that the posterior uncertainty estimates obtained based on prior sampling can be considered conservative estimates of the uncertainties obtained by directly sampling from the posterior distribution.
Solving probabilistic inverse problems rapidly with prior samples
NASA Astrophysics Data System (ADS)
Käufl, Paul; Valentine, Andrew; De Wit, Ralph; Trampert, Jeannot
2016-03-01
Owing to the increasing availability of computational resources, in recent years the probabilistic solution of non-linear, geophysical inverse problems by means of sampling methods has become increasingly feasible. Nevertheless, we still face situations in which a Monte Carlo approach is not practical. This is particularly true in cases where the evaluation of the forward problem is computationally intensive or where inversions have to be carried out repeatedly or in a timely manner, as in natural hazards monitoring tasks such as earthquake early warning. Here, we present an alternative to Monte Carlo sampling, in which inferences are entirely based on a set of prior samples-i.e. samples that have been obtained independent of a particular observed datum. This has the advantage that the computationally expensive sampling stage becomes separated from the inversion stage, and the set of prior samples-once obtained-can be reused for repeated evaluations of the inverse mapping without additional computational effort. This property is useful if the problem is such that repeated inversions of independent data have to be carried out. We formulate the inverse problem in a Bayesian framework and present a practical way to make posterior inferences based on a set of prior samples. We compare the prior sampling based approach to a Markov Chain Monte Carlo approach that samples from the posterior probability distribution. We show results for both a toy example, and a realistic seismological source parameter estimation problem. We find that the posterior uncertainty estimates obtained based on prior sampling can be considered conservative estimates of the uncertainties obtained by directly sampling from the posterior distribution.
A tutorial on inverse problems for anomalous diffusion processes
NASA Astrophysics Data System (ADS)
Jin, Bangti; Rundell, William
2015-03-01
Over the last two decades, anomalous diffusion processes in which the mean squares variance grows slower or faster than that in a Gaussian process have found many applications. At a macroscopic level, these processes are adequately described by fractional differential equations, which involves fractional derivatives in time or/and space. The fractional derivatives describe either history mechanism or long range interactions of particle motions at a microscopic level. The new physics can change dramatically the behavior of the forward problems. For example, the solution operator of the time fractional diffusion diffusion equation has only limited smoothing property, whereas the solution for the space fractional diffusion equation may contain weak singularity. Naturally one expects that the new physics will impact related inverse problems in terms of uniqueness, stability, and degree of ill-posedness. The last aspect is especially important from a practical point of view, i.e., stably reconstructing the quantities of interest. In this paper, we employ a formal analytic and numerical way, especially the two-parameter Mittag-Leffler function and singular value decomposition, to examine the degree of ill-posedness of several ‘classical’ inverse problems for fractional differential equations involving a Djrbashian-Caputo fractional derivative in either time or space, which represent the fractional analogues of that for classical integral order differential equations. We discuss four inverse problems, i.e., backward fractional diffusion, sideways problem, inverse source problem and inverse potential problem for time fractional diffusion, and inverse Sturm-Liouville problem, Cauchy problem, backward fractional diffusion and sideways problem for space fractional diffusion. It is found that contrary to the wide belief, the influence of anomalous diffusion on the degree of ill-posedness is not definitive: it can either significantly improve or worsen the conditioning of
A time domain sampling method for inverse acoustic scattering problems
NASA Astrophysics Data System (ADS)
Guo, Yukun; Hömberg, Dietmar; Hu, Guanghui; Li, Jingzhi; Liu, Hongyu
2016-06-01
This work concerns the inverse scattering problems of imaging unknown/inaccessible scatterers by transient acoustic near-field measurements. Based on the analysis of the migration method, we propose efficient and effective sampling schemes for imaging small and extended scatterers from knowledge of time-dependent scattered data due to incident impulsive point sources. Though the inverse scattering problems are known to be nonlinear and ill-posed, the proposed imaging algorithms are totally "direct" involving only integral calculations on the measurement surface. Theoretical justifications are presented and numerical experiments are conducted to demonstrate the effectiveness and robustness of our methods. In particular, the proposed static imaging functionals enhance the performance of the total focusing method (TFM) and the dynamic imaging functionals show analogous behavior to the time reversal inversion but without solving time-dependent wave equations.
Solving the Inverse-Square Problem with Complex Variables
ERIC Educational Resources Information Center
Gauthier, N.
2005-01-01
The equation of motion for a mass that moves under the influence of a central, inverse-square force is formulated and solved as a problem in complex variables. To find the solution, the constancy of angular momentum is first established using complex variables. Next, the complex position coordinate and complex velocity of the particle are assumed…
Toward precise solution of one-dimensional velocity inverse problems
Gray, S.; Hagin, F.
1980-01-01
A family of one-dimensional inverse problems are considered with the goal of reconstructing velocity profiles to reasonably high accuracy. The travel-time variable change is used together with an iteration scheme to produce an effective algorithm for computation. Under modest assumptions the scheme is shown to be convergent.
Computational methods for inverse problems in geophysics: inversion of travel time observations
Pereyra, V.; Keller, H.B.; Lee, W.H.K.
1980-01-01
General ways of solving various inverse problems are studied for given travel time observations between sources and receivers. These problems are separated into three components: (a) the representation of the unknown quantities appearing in the model; (b) the nonlinear least-squares problem; (c) the direct, two-point ray-tracing problem used to compute travel time once the model parameters are given. Novel software is described for (b) and (c), and some ideas given on (a). Numerical results obtained with artificial data and an implementation of the algorithm are also presented. ?? 1980.
Kılıç, Emre Eibert, Thomas F.
2015-05-01
An approach combining boundary integral and finite element methods is introduced for the solution of three-dimensional inverse electromagnetic medium scattering problems. Based on the equivalence principle, unknown equivalent electric and magnetic surface current densities on a closed surface are utilized to decompose the inverse medium problem into two parts: a linear radiation problem and a nonlinear cavity problem. The first problem is formulated by a boundary integral equation, the computational burden of which is reduced by employing the multilevel fast multipole method (MLFMM). Reconstructed Cauchy data on the surface allows the utilization of the Lorentz reciprocity and the Poynting's theorems. Exploiting these theorems, the noise level and an initial guess are estimated for the cavity problem. Moreover, it is possible to determine whether the material is lossy or not. In the second problem, the estimated surface currents form inhomogeneous boundary conditions of the cavity problem. The cavity problem is formulated by the finite element technique and solved iteratively by the Gauss–Newton method to reconstruct the properties of the object. Regularization for both the first and the second problems is achieved by a Krylov subspace method. The proposed method is tested against both synthetic and experimental data and promising reconstruction results are obtained.
NASA Astrophysics Data System (ADS)
Plestenjak, Bor; Gheorghiu, Călin I.; Hochstenbach, Michiel E.
2015-10-01
In numerous science and engineering applications a partial differential equation has to be solved on some fairly regular domain that allows the use of the method of separation of variables. In several orthogonal coordinate systems separation of variables applied to the Helmholtz, Laplace, or Schrödinger equation leads to a multiparameter eigenvalue problem (MEP); important cases include Mathieu's system, Lamé's system, and a system of spheroidal wave functions. Although multiparameter approaches are exploited occasionally to solve such equations numerically, MEPs remain less well known, and the variety of available numerical methods is not wide. The classical approach of discretizing the equations using standard finite differences leads to algebraic MEPs with large matrices, which are difficult to solve efficiently. The aim of this paper is to change this perspective. We show that by combining spectral collocation methods and new efficient numerical methods for algebraic MEPs it is possible to solve such problems both very efficiently and accurately. We improve on several previous results available in the literature, and also present a MATLAB toolbox for solving a wide range of problems.
NASA Astrophysics Data System (ADS)
Uhlmann, Gunther
2008-07-01
This volume represents the proceedings of the fourth Applied Inverse Problems (AIP) international conference and the first congress of the Inverse Problems International Association (IPIA) which was held in Vancouver, Canada, June 25 29, 2007. The organizing committee was formed by Uri Ascher, University of British Columbia, Richard Froese, University of British Columbia, Gary Margrave, University of Calgary, and Gunther Uhlmann, University of Washington, chair. The conference was part of the activities of the Pacific Institute of Mathematical Sciences (PIMS) Collaborative Research Group on inverse problems (http://www.pims.math.ca/scientific/collaborative-research-groups/past-crgs). This event was also supported by grants from NSF and MITACS. Inverse Problems (IP) are problems where causes for a desired or an observed effect are to be determined. They lie at the heart of scientific inquiry and technological development. The enormous increase in computing power and the development of powerful algorithms have made it possible to apply the techniques of IP to real-world problems of growing complexity. Applications include a number of medical as well as other imaging techniques, location of oil and mineral deposits in the earth's substructure, creation of astrophysical images from telescope data, finding cracks and interfaces within materials, shape optimization, model identification in growth processes and, more recently, modelling in the life sciences. The series of Applied Inverse Problems (AIP) Conferences aims to provide a primary international forum for academic and industrial researchers working on all aspects of inverse problems, such as mathematical modelling, functional analytic methods, computational approaches, numerical algorithms etc. The steering committee of the AIP conferences consists of Heinz Engl (Johannes Kepler Universität, Austria), Joyce McLaughlin (RPI, USA), William Rundell (Texas A&M, USA), Erkki Somersalo (Helsinki University of Technology
NASA Astrophysics Data System (ADS)
Uhlmann, Gunther
2008-07-01
This volume represents the proceedings of the fourth Applied Inverse Problems (AIP) international conference and the first congress of the Inverse Problems International Association (IPIA) which was held in Vancouver, Canada, June 25 29, 2007. The organizing committee was formed by Uri Ascher, University of British Columbia, Richard Froese, University of British Columbia, Gary Margrave, University of Calgary, and Gunther Uhlmann, University of Washington, chair. The conference was part of the activities of the Pacific Institute of Mathematical Sciences (PIMS) Collaborative Research Group on inverse problems (http://www.pims.math.ca/scientific/collaborative-research-groups/past-crgs). This event was also supported by grants from NSF and MITACS. Inverse Problems (IP) are problems where causes for a desired or an observed effect are to be determined. They lie at the heart of scientific inquiry and technological development. The enormous increase in computing power and the development of powerful algorithms have made it possible to apply the techniques of IP to real-world problems of growing complexity. Applications include a number of medical as well as other imaging techniques, location of oil and mineral deposits in the earth's substructure, creation of astrophysical images from telescope data, finding cracks and interfaces within materials, shape optimization, model identification in growth processes and, more recently, modelling in the life sciences. The series of Applied Inverse Problems (AIP) Conferences aims to provide a primary international forum for academic and industrial researchers working on all aspects of inverse problems, such as mathematical modelling, functional analytic methods, computational approaches, numerical algorithms etc. The steering committee of the AIP conferences consists of Heinz Engl (Johannes Kepler Universität, Austria), Joyce McLaughlin (RPI, USA), William Rundell (Texas A&M, USA), Erkki Somersalo (Helsinki University of Technology
Inverse AVO problem for a stack of layers
NASA Astrophysics Data System (ADS)
Malovichko, Liliya
2015-09-01
The problem of estimating thin layered model parameters by amplitude variation with offset (AVO) inversion has been studied. The motivation was resolving of the thin layers in inverted prestack seismic data as it contains more information on elastic properties of the subsurface than poststack seismic data. In this paper, an algorithm for solving the prestack inverse AVO problem in the case of multilayered media is derived. This algorithm is based on iterative corrections to the parameters of the initial model which tend to minimise the misfits between observed and synthetic seismograms. The synthetic seismograms are calculated using the reflection-transmission (RT)-matrices method, assuming a plane-wave with respect to the source position. A regularised Gauss-type algorithm for the inversion of prestack seismic data has been used. A differential seismogram computation algorithm to characterise the sensitivity of the seismic signal to the variations of a model parameter was used. The derived solution of the inverse problem is constructed in the time domain. This gives a slight advantage because it allows for visual control of the solution process. One can monitor the amplitude reduction of the data residual (difference between observed and synthetic seismograms) during the iteration process. Numerical examples show the accuracy and efficiency of the method.
Application of inverse heat conduction problem on temperature measurement
NASA Astrophysics Data System (ADS)
Zhang, X.; Zhou, G.; Dong, B.; Li, Q.; Liu, L. Q.
2013-09-01
For regenerative cooling devices, such as G-M refrigerator, pulse tube cooler or thermoacoustic cooler, the gas oscillating bring about temperature fluctuations inevitably, which is harmful in many applications requiring high stable temperatures. To find out the oscillating mechanism of the cooling temperature and improve the temperature stability of cooler, the inner temperature of the cold head has to be measured. However, it is difficult to measure the inner oscillating temperature of the cold head directly because the invasive temperature detectors may disturb the oscillating flow. Fortunately, the outer surface temperature of the cold head can be measured accurately by invasive temperature measurement techniques. In this paper, a mathematical model of inverse heat conduction problem is presented to identify the inner surface oscillating temperature of cold head according to the measured temperature of the outer surface in a GM cryocooler. Inverse heat conduction problem will be solved using control volume approach. Outer surface oscillating temperature could be used as input conditions of inverse problem and the inner surface oscillating temperature of cold head can be inversely obtained. A simple uncertainty analysis of the oscillating temperature measurement also will be provided.
SIAM conference on inverse problems: Geophysical applications. Final technical report
1995-12-31
This conference was the second in a series devoted to a particular area of inverse problems. The theme of this series is to discuss problems of major scientific importance in a specific area from a mathematical perspective. The theme of this symposium was geophysical applications. In putting together the program we tried to include a wide range of mathematical scientists and to interpret geophysics in as broad a sense as possible. Our speaker came from industry, government laboratories, and diverse departments in academia. We managed to attract a geographically diverse audience with participation from five continents. There were talks devoted to seismology, hydrology, determination of the earth`s interior on a global scale as well as oceanographic and atmospheric inverse problems.
A reduced basis Landweber method for nonlinear inverse problems
NASA Astrophysics Data System (ADS)
Garmatter, Dominik; Haasdonk, Bernard; Harrach, Bastian
2016-03-01
We consider parameter identification problems in parametrized partial differential equations (PDEs). These lead to nonlinear ill-posed inverse problems. One way of solving them is using iterative regularization methods, which typically require numerous amounts of forward solutions during the solution process. In this article we consider the nonlinear Landweber method and couple it with the reduced basis method as a model order reduction technique in order to reduce the overall computational time. In particular, we consider PDEs with a high-dimensional parameter space, which are known to pose difficulties in the context of reduced basis methods. We present a new method that is able to handle such high-dimensional parameter spaces by combining the nonlinear Landweber method with adaptive online reduced basis updates. It is then applied to the inverse problem of reconstructing the conductivity in the stationary heat equation.
Effects of geometric uncertainty on the inverse EEG problem
NASA Astrophysics Data System (ADS)
Weinstein, David M.; Johnson, Christopher R.
1997-12-01
A standard method for noninvasively computing neurocortical potentials from potentials measured on the scalp surface is to solve the problem on a generalized geometry and map the results back to the true model. This solution to the inverse EEG problem has been employed using spherical and, more recently, generic cranial models as templates. In the case of the most complex spherical models, the patient's skin, bone, cerebrospinal fluid, gray matter and white matter surfaces are mapped onto concentric spheres. The simplicity of the spherical domain allows for an analytic solution to the surface mapping inverse problem; however, the inaccuracy of such a solution challenges its clinical value. Similarly, solving the problem on a predefined generic model also holds computational allure--the generic model can be hand-picked to reduce the ill-conditioning of the problem. However, we suggest that such results from generic models are still not sufficiently accurate to be of general clinical use. In our paper, we evaluate the impact of varying both model accuracy and model complexity on the inverse cortical mapping. Small modeling perturbations (as might be introduced from noisy or under-sampled data) are shown to have large and detrimental effects on the quality of the solution.
An Inverse Problem Approach for Elasticity Imaging through Vibroacoustics
Aguilo, Miguel A.; Brigham, J. C.; Aquino, W.; Fatemi, M.
2011-01-01
A new methodology for estimating the spatial distribution of elastic moduli using the steady-state dynamic response of solids immersed in fluids is presented. The technique relies on the ensuing acoustic pressure field from a remotely excited solid to inversely estimate the spatial distribution of Young’s modulus. This work proposes the use of Gaussian radial basis functions (GRBF) to represent the spatial variation of elastic moduli. GRBF are shown to possess the advantage of representing smooth functions with quasi-compact support, and can efficiently represent elastic moduli distributions such as those that occur in soft biological tissue in the presence of tumors. The direct problem consists of a coupled acoustic-structure interaction boundary value problem solved in the frequency domain using the finite element method. The inverse problem is cast as an optimization problem in which the objective function is defined as a measure of discrepancy between an experimentally measured response and a finite element representation of the system. Non-gradient based optimization algorithms in combination with a divide and conquer strategy are used to solve the resulting optimization problem. The feasibility of the proposed approach is demonstrated through a series of numerical and a physical experiment. For comparison purposes, the surface velocity response was also used for the inverse characterization as the measured response in place of the acoustic pressure. PMID:20335092
A non-local free boundary problem arising in a theory of financial bubbles
Berestycki, Henri; Monneau, Regis; Scheinkman, José A.
2014-01-01
We consider an evolution non-local free boundary problem that arises in the modelling of speculative bubbles. The solution of the model is the speculative component in the price of an asset. In the framework of viscosity solutions, we show the existence and uniqueness of the solution. We also show that the solution is convex in space, and establish several monotonicity properties of the solution and of the free boundary with respect to parameters of the problem. To study the free boundary, we use, in particular, the fact that the odd part of the solution solves a more standard obstacle problem. We show that the free boundary is and describe the asymptotics of the free boundary as c, the cost of transacting the asset, goes to zero. PMID:25288815
Two hybrid regularization frameworks for solving the electrocardiography inverse problem
NASA Astrophysics Data System (ADS)
Jiang, Mingfeng; Xia, Ling; Shou, Guofa; Liu, Feng; Crozier, Stuart
2008-09-01
In this paper, two hybrid regularization frameworks, LSQR-Tik and Tik-LSQR, which integrate the properties of the direct regularization method (Tikhonov) and the iterative regularization method (LSQR), have been proposed and investigated for solving ECG inverse problems. The LSQR-Tik method is based on the Lanczos process, which yields a sequence of small bidiagonal systems to approximate the original ill-posed problem and then the Tikhonov regularization method is applied to stabilize the projected problem. The Tik-LSQR method is formulated as an iterative LSQR inverse, augmented with a Tikhonov-like prior information term. The performances of these two hybrid methods are evaluated using a realistic heart-torso model simulation protocol, in which the heart surface source method is employed to calculate the simulated epicardial potentials (EPs) from the action potentials (APs), and then the acquired EPs are used to calculate simulated body surface potentials (BSPs). The results show that the regularized solutions obtained by the LSQR-Tik method are approximate to those of the Tikhonov method, the computational cost of the LSQR-Tik method, however, is much less than that of the Tikhonov method. Moreover, the Tik-LSQR scheme can reconstruct the epcicardial potential distribution more accurately, specifically for the BSPs with large noisy cases. This investigation suggests that hybrid regularization methods may be more effective than separate regularization approaches for ECG inverse problems.
Forward and inverse problems in fundamental and applied magnetohydrodynamics
NASA Astrophysics Data System (ADS)
Giesecke, Andre; Stefani, Frank; Wondrak, Thomas; Xu, Mingtian
2013-03-01
This minireview summarizes the recent efforts to solve forward and inverse problems as they occur in different branches of fundamental and applied magnetohydrodynamics. For the forward problem, the main focus is on the numerical treatment of induction processes, including self-excitation of magnetic fields in non-spherical domains and/or under the influence of non-homogeneous material parameters. As an important application of the developed numerical schemes, the functioning of the von-Kármán-sodium (VKS) dynamo experiment is shown to depend crucially on the presence of soft-iron impellers. As for the inverse problem, the main focus is on the mathematical background and some initial practical applications of contactless inductive flow tomography (CIFT), in which flow induced magnetic field perturbations are utilized to reconstruct the velocity field. The promises of CIFT for flow field monitoring in the continuous casting of steel are substantiated by results obtained at a test rig with a low-melting liquid metal. While CIFT is presently restricted to flows with low magnetic Reynolds numbers, some selected problems from non-linear inverse dynamo theory, with possible applications to geo- and astrophysics, are also discussed.
NASA Astrophysics Data System (ADS)
Kuchment, Peter; Steinhauer, Dustin
2015-12-01
In the previous paper (Kuchment and Steinhauer in Inverse Probl 28(8):084007, 2012), the authors introduced a simple procedure that allows one to detect whether and explain why internal information arising in several novel coupled physics (hybrid) imaging modalities could turn extremely unstable techniques, such as optical tomography or electrical impedance tomography, into stable, good-resolution procedures. It was shown that in all cases of interest, the Fréchet derivative of the forward mapping is a pseudo-differential operator with an explicitly computable principal symbol. If one can set up the imaging procedure in such a way that the symbol is elliptic, this would indicate that the problem was stabilized. In the cases when the symbol is not elliptic, the technique suggests how to change the procedure (e.g., by adding extra measurements) to achieve ellipticity. In this article, we consider the situation arising in acousto-optical tomography (also called ultrasound modulated optical tomography), where the internal data available involves the Green's function, and thus depends globally on the unknown parameter(s) of the equation and its solution. It is shown that the technique of (Kuchment and Steinhauer in Inverse Probl 28(8):084007, 2012) can be successfully adopted to this situation as well. A significant part of the article is devoted to results on generic uniqueness for the linearized problem in a variety of situations, including those arising in acousto-electric and quantitative photoacoustic tomography.
Solving the inverse problem of noise-driven dynamic networks.
Zhang, Zhaoyang; Zheng, Zhigang; Niu, Haijing; Mi, Yuanyuan; Wu, Si; Hu, Gang
2015-01-01
Nowadays, massive amounts of data are available for analysis in natural and social systems and the tasks to depict system structures from the data, i.e., the inverse problems, become one of the central issues in wide interdisciplinary fields. In this paper, we study the inverse problem of dynamic complex networks driven by white noise. A simple and universal inference formula of double correlation matrices and noise-decorrelation (DCMND) method is derived analytically, and numerical simulations confirm that the DCMND method can accurately depict both network structures and noise correlations by using available output data only. This inference performance has never been regarded possible by theoretical derivation, numerical computation, and experimental design. PMID:25679664
Changes in habitat of fish populations: An inverse problem.
Levere, Kimberly M
2016-08-01
Mathematical modelling applies to a wide variety of application areas, and is an active area of research in many disciplines. It is often the case that accurate depiction of real-world phenomena require increasingly complex models. Unfortunately, this increased complexity in a model causes great difficulty when seeking solutions. What is more, developing a model with known parameters that produces results consistent with observed behaviors may prove to be a difficult or even impossible task. These difficulties have brought about an interest in inverse problems. In this paper we utilize a collage-based approach to solve an inverse problem for a model for the migration of three fish species through floodplain waters. A derivation of the mathematical model is presented and a generalized collage method is discussed and applied to this model to recover diffusion parameters. Theoretical and numerical particulars are discussed and results are presented. PMID:27245383
Inverse problems in the modeling of vibrations of flexible beams
NASA Technical Reports Server (NTRS)
Banks, H. T.; Powers, R. K.; Rosen, I. G.
1987-01-01
The formulation and solution of inverse problems for the estimation of parameters which describe damping and other dynamic properties in distributed models for the vibration of flexible structures is considered. Motivated by a slewing beam experiment, the identification of a nonlinear velocity dependent term which models air drag damping in the Euler-Bernoulli equation is investigated. Galerkin techniques are used to generate finite dimensional approximations. Convergence estimates and numerical results are given. The modeling of, and related inverse problems for the dynamics of a high pressure hose line feeding a gas thruster actuator at the tip of a cantilevered beam are then considered. Approximation and convergence are discussed and numerical results involving experimental data are presented.
Lycurgus Cup: inverse problem using photographs for characterization of matter.
Barchiesi, Dominique
2015-08-01
Photographs of the Lycurgus Cup with a source light inside and outside exhibit purple and green colors, respectively (dichroism). A model relying on the scattering of light to colors in the photographs is proposed and used within an inverse problem algorithm, to deduce radius and composition of metallic particles, and the refractive index of the surrounding glass medium. The inverse problem algorithm is based on a hybridization of particle swarm optimization and of the simulated annealing methods. The results are compared to experimental measurements on a small sample of glass. The linear laws that are deduced from sets of possible parameters producing the same color in the photographs help simplify the understanding of phenomena. The proportion of silver to gold in nanoparticles is found to be in agreement, but a large proportion of copper is also found. The retrieved refractive index of the surrounding glass is close to 2. PMID:26367298
Efficient algorithms for linear dynamic inverse problems with known motion
NASA Astrophysics Data System (ADS)
Hahn, B. N.
2014-03-01
An inverse problem is called dynamic if the object changes during the data acquisition process. This occurs e.g. in medical applications when fast moving organs like the lungs or the heart are imaged. Most regularization methods are based on the assumption that the object is static during the measuring procedure. Hence, their application in the dynamic case often leads to serious motion artefacts in the reconstruction. Therefore, an algorithm has to take into account the temporal changes of the investigated object. In this paper, a reconstruction method that compensates for the motion of the object is derived for dynamic linear inverse problems. The algorithm is validated at numerical examples from computerized tomography.
An inverse finance problem for estimation of the volatility
NASA Astrophysics Data System (ADS)
Neisy, A.; Salmani, K.
2013-01-01
Black-Scholes model, as a base model for pricing in derivatives markets has some deficiencies, such as ignoring market jumps, and considering market volatility as a constant factor. In this article, we introduce a pricing model for European-Options under jump-diffusion underlying asset. Then, using some appropriate numerical methods we try to solve this model with integral term, and terms including derivative. Finally, considering volatility as an unknown parameter, we try to estimate it by using our proposed model. For the purpose of estimating volatility, in this article, we utilize inverse problem, in which inverse problem model is first defined, and then volatility is estimated using minimization function with Tikhonov regularization.
Inverse problems for homogeneous transport equations: II. The multidimensional case
NASA Astrophysics Data System (ADS)
Bal, Guillaume
2000-08-01
A companion paper by Bal (Bal G 2000 Inverse Problems 16 997) and this paper are parts I and II of a series dealing with the reconstruction from boundary measurements of the scattering operator of homogeneous linear transport equations. This part II deals with the case of convex bounded domains in dimensions higher than one. We distinguish the analysis of smooth boundaries from that of boundaries with discontinuities such as corners. We propose a reconstruction in the case of degenerate symmetric scattering operators and show the well-posedness of the inverse problem. The proof of well-posedness is based on a decomposition of angular moments of the transport solution into unbounded and bounded components. This decomposition allows us to show the linear independence of a sufficiently large number of angular moments of the transport solution that are used to construct an invertible system for the scattering coefficients to be reconstructed.
Solving the inverse problem of noise-driven dynamic networks
NASA Astrophysics Data System (ADS)
Zhang, Zhaoyang; Zheng, Zhigang; Niu, Haijing; Mi, Yuanyuan; Wu, Si; Hu, Gang
2015-01-01
Nowadays, massive amounts of data are available for analysis in natural and social systems and the tasks to depict system structures from the data, i.e., the inverse problems, become one of the central issues in wide interdisciplinary fields. In this paper, we study the inverse problem of dynamic complex networks driven by white noise. A simple and universal inference formula of double correlation matrices and noise-decorrelation (DCMND) method is derived analytically, and numerical simulations confirm that the DCMND method can accurately depict both network structures and noise correlations by using available output data only. This inference performance has never been regarded possible by theoretical derivation, numerical computation, and experimental design.
Diffuse interface methods for inverse problems: case study for an elliptic Cauchy problem
NASA Astrophysics Data System (ADS)
Burger, Martin; Løseth Elvetun, Ole; Schlottbom, Matthias
2015-12-01
Many inverse problems have to deal with complex, evolving and often not exactly known geometries, e.g. as domains of forward problems modeled by partial differential equations. This makes it desirable to use methods which are robust with respect to perturbed or not well resolved domains, and which allow for efficient discretizations not resolving any fine detail of those geometries. For forward problems in partial differential equations methods based on diffuse interface representations have gained strong attention in the last years, but so far they have not been considered systematically for inverse problems. In this work we introduce a diffuse domain method as a tool for the solution of variational inverse problems. As a particular example we study ECG inversion in further detail. ECG inversion is a linear inverse source problem with boundary measurements governed by an anisotropic diffusion equation, which naturally cries for solutions under changing geometries, namely the beating heart. We formulate a regularization strategy using Tikhonov regularization and, using standard source conditions, we prove convergence rates. A special property of our approach is that not only operator perturbations are introduced by the diffuse domain method, but more important we have to deal with topologies which depend on a parameter \\varepsilon in the diffuse domain method, i.e. we have to deal with \\varepsilon -dependent forward operators and \\varepsilon -dependent norms. In particular the appropriate function spaces for the unknown and the data depend on \\varepsilon . This prevents the application of some standard convergence techniques for inverse problems, in particular interpreting the perturbations as data errors in the original problem does not yield suitable results. We consequently develop a novel approach based on saddle-point problems. The numerical solution of the problem is discussed as well and results for several computational experiments are reported. In
Some special cases of the electromagnetic inverse problem
NASA Technical Reports Server (NTRS)
Weston, V. H.
1972-01-01
A review of exact techniques for determining the surface of a three-dimensional perfectly conducting body is given, followed by some new results on the uniqueness question concerning the number of measurements that may be required to explicitly determine the surface of the body. It is then shown that the inhomogeneous but spherically symmetric dielectric electromagnetic case is reducible to a scalar inverse problem that can be treated by known techniques.
Combined approach to the inverse protein folding problem. Final report
Ruben A. Abagyan
2000-06-01
The main scientific contribution of the project ''Combined approach to the inverse protein folding problem'' submitted in 1996 and funded by the Department of Energy in 1997 is the formulation and development of the idea of the multilink recognition method for identification of functional and structural homologues of newly discovered genes. This idea became very popular after they first announced it and used it in prediction of the threading targets for the CASP2 competition (Critical Assessment of Structure Prediction).
Inverse problems of NEO photometry: Imaging the NEO population
NASA Astrophysics Data System (ADS)
Kaasalainen1, Mikko; Durech, Josef
2007-05-01
Photometry is the main source of information on NEOs (and other asteroids) en masse. Surveys such as Pan-STARRS and LSST will produce colossal photometric databases that can readily be used for mapping the physical characteristics of the whole asteroid population. These datasets are efficiently enriched by any additional dense photometric or other observations. Due to their quickly changing geometries with respect to the Earth, NEOs are the subpopulation that can be mapped the fastest. I review the state of the art in the construction of physical asteroid models from sparse and/or dense photometric data (that can also be combined with other data modes). The models describe the shapes, spin states, scattering properties and surface structure of the targets, and are the solutions of inverse problems necessarily involving comprehensive mathematical analysis. I sum up what we can and cannot get from photometric data, and how all this is done in practice. I also discuss the new freely available software package for solving photometric inverse problems (soon to be released). The analysis of photometric datasets will very soon become an automated industry, resulting in tens of thousands of asteroid models, a large portion of them NEOs. The computational effort in this is considerable in both computer and human time, which means that a large portion of the targets is likely to be analyzed only once. This, again, means that we have to have a good understanding of the reliability of our models, and this is impossible without a thorough understanding of the mathematical nature of the inverse problem(s) involved. Very important concepts are the uniqueness and stability of the solution, the parameter spaces, the so-called inverse crimes in simulations and error prediction, and the domination of systematic errors over random ones.
Asynchronous global optimization techniques for medium and large inversion problems
Pereyra, V.; Koshy, M.; Meza, J.C.
1995-04-01
We discuss global optimization procedures adequate for seismic inversion problems. We explain how to save function evaluations (which may involve large scale ray tracing or other expensive operations) by creating a data base of information on what parts of parameter space have already been inspected. It is also shown how a correct parallel implementation using PVM speeds up the process almost linearly with respect to the number of processors, provided that the function evaluations are expensive enough to offset the communication overhead.
Eddy-current NDE inverse problem with sparse grid algorithm
NASA Astrophysics Data System (ADS)
Zhou, Liming; Sabbagh, Harold A.; Sabbagh, Elias H.; Murphy, R. Kim; Bernacchi, William; Aldrin, John C.; Forsyth, David; Lindgren, Eric
2016-02-01
In model-based inverse problems, the unknown parameters (such as length, width, depth) need to be estimated. When the unknown parameters are few, the conventional mathematical methods are suitable. But the increasing number of unknown parameters will make the computation become heavy. To reduce the burden of computation, the sparse grid algorithm was used in our work. As a result, we obtain a powerful interpolation method that requires significantly fewer support nodes than conventional interpolation on a full grid.
NASA Astrophysics Data System (ADS)
Roul, Pradip
2016-04-01
The paper deals with a numerical technique for solving nonlinear singular boundary value problems arising in various physical models. First, we convert the original problem to an equivalent integral equation to surmount the singularity and employ afterward the boundary condition to compute the undetermined coefficient. Finally, the integral equation without undetermined coefficient is treated using homotopy perturbation method. The present method is implemented on three physical model examples: i) thermal explosions; ii) steady-state oxygen diffusion in a spherical shell; iii) the equilibrium of the isothermal gas sphere. The results obtained by the present method are compared with that obtained using finite-difference method, B-spline method and a numerical technique based on the direct integration method, and comparison reveals that the proposed method with few solution components produces similar results and the method is computationally efficient than others.
Introduction to the 30th volume of Inverse Problems
NASA Astrophysics Data System (ADS)
Louis, Alfred K.
2014-01-01
The field of inverse problems is a fast-developing domain of research originating from the practical demands of finding the cause when a result is observed. The woodpecker, searching for insects, is probing a tree using sound waves: the information searched for is whether there is an insect or not, hence a 0-1 decision. When the result has to contain more information, ad hoc solutions are not at hand and more sophisticated methods have to be developed. Right from its first appearance, the field of inverse problems has been characterized by an interdisciplinary nature: the interpretation of measured data, reinforced by mathematical models serving the analyzing questions of observability, stability and resolution, developing efficient, stable and accurate algorithms to gain as much information as possible from the input and to feedback to the questions of optimal measurement configuration. As is typical for a new area of research, facets of it are separated and studied independently. Hence, fields such as the theory of inverse scattering, tomography in general and regularization methods have developed. However, all aspects have to be reassembled to arrive at the best possible solution to the problem at hand. This development is reflected by the first and still leading journal in the field, Inverse Problems. Founded by pioneers Roy Pike from London and Pierre Sabatier from Montpellier, who enjoyably describes the journal's nascence in his book Rêves et Combats d'un Enseignant-Chercheur, Retour Inverse [1], the journal has developed successfully over the last few decades. Neither the Editors-in-Chief, formerly called Honorary Editors, nor the board or authors could have set the path to success alone. Their fruitful interplay, complemented by the efficient and highly competent publishing team at IOP Publishing, has been fundamental. As such it is my honor and pleasure to follow my renowned colleagues Pierre Sabatier, Mario Bertero, Frank Natterer, Alberto Grünbaum and
NASA Astrophysics Data System (ADS)
Ivanyshyn Yaman, Olha; Le Louër, Frédérique
2016-09-01
This paper deals with the material derivative analysis of the boundary integral operators arising from the scattering theory of time-harmonic electromagnetic waves and its application to inverse problems. We present new results using the Piola transform of the boundary parametrisation to transport the integral operators on a fixed reference boundary. The transported integral operators are infinitely differentiable with respect to the parametrisations and simplified expressions of the material derivatives are obtained. Using these results, we extend a nonlinear integral equations approach developed for solving acoustic inverse obstacle scattering problems to electromagnetism. The inverse problem is formulated as a pair of nonlinear and ill-posed integral equations for the unknown boundary representing the boundary condition and the measurements, for which the iteratively regularized Gauss-Newton method can be applied. The algorithm has the interesting feature that it avoids the numerous numerical solution of boundary value problems at each iteration step. Numerical experiments are presented in the special case of star-shaped obstacles.
The inverse problems of wing panel manufacture processes
NASA Astrophysics Data System (ADS)
Oleinikov, A. I.; Bormotin, K. S.
2013-12-01
It is shown that inverse problems of steady-state creep bending of plates in both the geometrically linear and nonlinear formulations can be represented in a variational formulation. Steady-state values of the obtained functionals corresponding to the solutions of the problems of inelastic deformation and springback are determined by applying a finite element procedure to the functionals. Optimal laws of creep deformation are formulated using the criterion of minimizing damage in the functionals of the inverse problems. The formulated problems are reduced to the problems solved by the finite element method using MSC.Marc software. Currently, forming of light metals poses tremendous challenges due to their low ductility at room temperature and their unusual deformation characteristics at hot-cold work: strong asymmetry between tensile and compressive behavior, and a very pronounced anisotropy. We used the constitutive models of steady-state creep of initially transverse isotropy structural materials the kind of the stress state has influence. The paper gives basics of the developed computer-aided system of design, modeling, and electronic simulation targeting the processes of manufacture of wing integral panels. The modeling results can be used to calculate the die tooling, determine the panel processibility, and control panel rejection in the course of forming.
Stochastic reduced order models for inverse problems under uncertainty
Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.
2014-01-01
This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115
The inverse problems of wing panel manufacture processes
Oleinikov, A. I.; Bormotin, K. S.
2013-12-16
It is shown that inverse problems of steady-state creep bending of plates in both the geometrically linear and nonlinear formulations can be represented in a variational formulation. Steady-state values of the obtained functionals corresponding to the solutions of the problems of inelastic deformation and springback are determined by applying a finite element procedure to the functionals. Optimal laws of creep deformation are formulated using the criterion of minimizing damage in the functionals of the inverse problems. The formulated problems are reduced to the problems solved by the finite element method using MSC.Marc software. Currently, forming of light metals poses tremendous challenges due to their low ductility at room temperature and their unusual deformation characteristics at hot-cold work: strong asymmetry between tensile and compressive behavior, and a very pronounced anisotropy. We used the constitutive models of steady-state creep of initially transverse isotropy structural materials the kind of the stress state has influence. The paper gives basics of the developed computer-aided system of design, modeling, and electronic simulation targeting the processes of manufacture of wing integral panels. The modeling results can be used to calculate the die tooling, determine the panel processibility, and control panel rejection in the course of forming.
Including geological information in the inverse problem of palaeothermal reconstruction
NASA Astrophysics Data System (ADS)
Trautner, S.; Nielsen, S. B.
2003-04-01
A reliable reconstruction of sediment thermal history is of central importance to the assessment of hydrocarbon potential and the understanding of basin evolution. However, only rarely do sedimentation history and borehole data in the form of present day temperatures and vitrinite reflectance constrain the past thermal evolution to a useful level of accuracy (Gallagher and Sambridge,1992; Nielsen,1998; Trautner and Nielsen,2003). This is reflected in the inverse solutions to the problem of determining heat flow history from borehole data: The recent heat flow is constrained by data while older values are governed by the chosen a prior heat flow. In this paper we reduce this problem by including geological information in the inverse problem. Through a careful analysis of geological and geophysical data the timing of the tectonic processes, which may influence heat flow, can be inferred. The heat flow history is then parameterised to allow for the temporal variations characteristic of the different tectonic events. The inversion scheme applies a Markov chain Monte Carlo (MCMC) approach (Nielsen and Gallagher, 1999; Ferrero and Gallagher,2002), which efficiently explores the model space and futhermore samples the posterior probability distribution of the model. The technique is demonstrated on wells in the northern North Sea with emphasis on the stretching event in Late Jurassic. The wells are characterised by maximum sediment temperature at the present day, which is the worst case for resolution of the past thermal history because vitrinite reflectance is determined mainly by the maximum temperature. Including geological information significantly improves the thermal resolution. Ferrero, C. and Gallagher,K.,2002. Stochastic thermal history modelling.1. Constraining heat flow histories and their uncertainty. Marine and Petroleum Geology, 19, 633-648. Gallagher,K. and Sambridge, M., 1992. The resolution of past heat flow in sedimentary basins from non-linear inversion
Inverse problems with Poisson data: statistical regularization theory, applications and algorithms
NASA Astrophysics Data System (ADS)
Hohage, Thorsten; Werner, Frank
2016-09-01
Inverse problems with Poisson data arise in many photonic imaging modalities in medicine, engineering and astronomy. The design of regularization methods and estimators for such problems has been studied intensively over the last two decades. In this review we give an overview of statistical regularization theory for such problems, the most important applications, and the most widely used algorithms. The focus is on variational regularization methods in the form of penalized maximum likelihood estimators, which can be analyzed in a general setup. Complementing a number of recent convergence rate results we will establish consistency results. Moreover, we discuss estimators based on a wavelet-vaguelette decomposition of the (necessarily linear) forward operator. As most prominent applications we briefly introduce Positron emission tomography, inverse problems in fluorescence microscopy, and phase retrieval problems. The computation of a penalized maximum likelihood estimator involves the solution of a (typically convex) minimization problem. We also review several efficient algorithms which have been proposed for such problems over the last five years.
Inference in infinite-dimensional inverse problems - Discretization and duality
NASA Technical Reports Server (NTRS)
Stark, Philip B.
1992-01-01
Many techniques for solving inverse problems involve approximating the unknown model, a function, by a finite-dimensional 'discretization' or parametric representation. The uncertainty in the computed solution is sometimes taken to be the uncertainty within the parametrization; this can result in unwarranted confidence. The theory of conjugate duality can overcome the limitations of discretization within the 'strict bounds' formalism, a technique for constructing confidence intervals for functionals of the unknown model incorporating certain types of prior information. The usual computational approach to strict bounds approximates the 'primal' problem in a way that the resulting confidence intervals are at most long enough to have the nominal coverage probability. There is another approach based on 'dual' optimization problems that gives confidence intervals with at least the nominal coverage probability. The pair of intervals derived by the two approaches bracket a correct confidence interval. The theory is illustrated with gravimetric, seismic, geomagnetic, and helioseismic problems and a numerical example in seismology.
Source localization in electromyography using the inverse potential problem
NASA Astrophysics Data System (ADS)
van den Doel, Kees; Ascher, Uri M.; Pai, Dinesh K.
2011-02-01
We describe an efficient method for reconstructing the activity in human muscles from an array of voltage sensors on the skin surface. MRI is used to obtain morphometric data which are segmented into muscle tissue, fat, bone and skin, from which a finite element model for volume conduction is constructed. The inverse problem of finding the current sources in the muscles is solved using a careful regularization technique which adds a priori information, yielding physically reasonable solutions from among those that satisfy the basic potential problem. Several regularization functionals are considered and numerical experiments on a 2D test model are performed to determine which performs best. The resulting scheme leads to numerical difficulties when applied to large-scale 3D problems. We clarify the nature of these difficulties and provide a method to overcome them, which is shown to perform well in the large-scale problem setting.
Inverse problem for in vivo NMR spatial localization
Hasenfeld, A.C.
1985-11-01
The basic physical problem of NMR spatial localization is considered. To study diseased sites, one must solve the problem of adequately localizing the NMR signal. We formulate this as an inverse problem. As the NMR Bloch equations determine the motion of nuclear spins in applied magnetic fields, a theoretical study is undertaken to answer the question of how to design magnetic field configurations to achieve these localized excited spin populations. Because of physical constraints in the production of the relevant radiofrequency fields, the problem factors into a temporal one and a spatial one. We formulate the temporal problem as a nonlinear transformation, called the Bloch Transform, from the rf input to the magnetization response. In trying to invert this transformation, both linear (for the Fourier Transform) and nonlinear (for the Bloch Transform) modes of radiofrequency excitation are constructed. The spatial problem is essentially a statics problem for the Maxwell equations of electromagnetism, as the wavelengths of the radiation considered are on the order of ten meters, and so propagation effects are negligible. In the general case, analytic solutions are unavailable, and so the methods of computer simulation are used to map the rf field spatial profiles. Numerical experiments are also performed to verify the theoretical analysis, and experimental confirmation of the theory is carried out on the 0.5 Tesla IBM/Oxford Imaging Spectrometer at the LBL NMR Medical Imaging Facility. While no explicit inverse is constructed to ''solve'' this problem, the combined theoretical/numerical analysis is validated experimentally, justifying the approximations made. 56 refs., 31 figs.
Inverse problems in heterogeneous and fractured media using peridynamics
Turner, Daniel Z.; van Bloemen Waanders, Bart G.; Parks, Michael L.
2015-12-10
The following work presents an adjoint-based methodology for solving inverse problems in heterogeneous and fractured media using state-based peridynamics. We show that the inner product involving the peridynamic operators is self-adjoint. The proposed method is illustrated for several numerical examples with constant and spatially varying material parameters as well as in the context of fractures. We also present a framework for obtaining material parameters by integrating digital image correlation (DIC) with inverse analysis. This framework is demonstrated by evaluating the bulk and shear moduli for a sample of nuclear graphite using digital photographs taken during the experiment. The resulting measured values correspond well with other results reported in the literature. Lastly, we show that this framework can be used to determine the load state given observed measurements of a crack opening. Furthermore, this type of analysis has many applications in characterizing subsurface stress-state conditions given fracture patterns in cores of geologic material.
Efficient solution of an inverse problem in cell population dynamics
NASA Astrophysics Data System (ADS)
Groh, Andreas; Krebs, Jochen; Wagner, Mathias
2011-06-01
In this paper, a size-structured model for cell division is examined and the question of determining the division (birth) rate from a measurable stable size distribution of the population is addressed. This inverse problem can be formulated as a differential-dilation equation. We propose a novel solution scheme based on mollification. The method of approximate inverse allows us to shift the derivative from the data to a precomputable reconstruction kernel. To comprise all available a priori information, a presmoothing step based on regression in reproducing kernel Hilbert spaces is introduced. We establish an error theory for the emerging algorithm, prove convergence and deduce a parameter strategy. The results are substantiated with extensive numerical tests both for artificial and real data based on proliferating tumor cells.
Principal Component Geostatistical Approach for large-dimensional inverse problems
NASA Astrophysics Data System (ADS)
Kitanidis, P. K.; Lee, J.
2014-07-01
The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best.
Principal Component Geostatistical Approach for large-dimensional inverse problems
Kitanidis, P K; Lee, J
2014-01-01
The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best. PMID:25558113
NASA Astrophysics Data System (ADS)
Abdelazeem, Maha; Gobashy, Mohamed
2015-04-01
The magnetic inverse problem is, intrinsically, non-unique and its numerical solution is unstable. This means that any small perturbation in the data (noise) causes large variation in the solution. This ill-posedness is not only due to complex geological situations, but it may arise because of ill-conditioned kernel matrix. Procedures adopted to stabilize the inversion of ill-posed problem are called regularization, so the selection of regularization parameter is very important to invert the earth model causing the measured magnetic field. Two strategies are commonly used, techniques based on Tikhonov formula and techniques using the trust region sub-problem TRS and the controlling factor will be the radius of such region. In this study, the two categories are compared to examine the stability of solutions with noise. A MATLAB-based inversion code is implemented and tested on some synthetic total magnetic fields with different noise levels added to simulate real fields. The capability of such techniques have been further tested by applying it to real data.
Numerical solution of the imprecisely defined inverse heat conduction problem
NASA Astrophysics Data System (ADS)
Smita, Tapaswini; Chakraverty, S.; Diptiranjan, Behera
2015-05-01
This paper investigates the numerical solution of the uncertain inverse heat conduction problem. Uncertainties present in the system parameters are modelled through triangular convex normalized fuzzy sets. In the solution process, double parametric forms of fuzzy numbers are used with the variational iteration method (VIM). This problem first computes the uncertain temperature distribution in the domain. Next, when the uncertain temperature measurements in the domain are known, the functions describing the uncertain temperature and heat flux on the boundary are reconstructed. Related example problems are solved using the present procedure. We have also compared the present results with those in [Inf. Sci. (2008) 178 1917] along with homotopy perturbation method (HPM) and [Int. Commun. Heat Mass Transfer (2012) 39 30] in the special cases to demonstrate the validity and applicability.
NASA Technical Reports Server (NTRS)
Fymat, A. L.
1976-01-01
The paper studies the inversion of the radiative transfer equation describing the interaction of electromagnetic radiation with atmospheric aerosols. The interaction can be considered as the propagation in the aerosol medium of two light beams: the direct beam in the line-of-sight attenuated by absorption and scattering, and the diffuse beam arising from scattering into the viewing direction, which propagates more or less in random fashion. The latter beam has single scattering and multiple scattering contributions. In the former case and for single scattering, the problem is reducible to first-kind Fredholm equations, while for multiple scattering it is necessary to invert partial integrodifferential equations. A nonlinear minimization search method, applicable to the solution of both types of problems has been developed, and is applied here to the problem of monitoring aerosol pollution, namely the complex refractive index and size distribution of aerosol particles.
Structure-approximating inverse protein folding problem in the 2D HP model.
Gupta, Arvind; Manuch, Ján; Stacho, Ladislav
2005-12-01
The inverse protein folding problem is that of designing an amino acid sequence which has a particular native protein fold. This problem arises in drug design where a particular structure is necessary to ensure proper protein-protein interactions. In this paper, we show that in the 2D HP model of Dill it is possible to solve this problem for a broad class of structures. These structures can be used to closely approximate any given structure. One of the most important properties of a good protein (in drug design) is its stability--the aptitude not to fold simultaneously into other structures. We show that for a number of basic structures, our sequences have a unique fold. PMID:16379538
Solving the inverse problem of magnetisation-stress resolution
NASA Astrophysics Data System (ADS)
Staples, S. G. H.; Vo, C.; Cowell, D. M. J.; Freear, S.; Ives, C.; Varcoe, B. T. H.
2013-04-01
Magnetostriction in various metals has been known since 1842, recently the focus has shifted away from ferrous metals, towards materials with a straightforward or exaggerated stress magnetostriction relationship. However, there is an increasing interest in understanding ferrous metal relationships, especially steels, because of its widespread use in building structures, transportation infrastructure, and pipelines. The aim of this paper is to solve the inverse problem of determining stress from an observed magnetic field which implies a given magnetic structure and to demonstrate that theoretical calculations using a multi-physics modeling technique agree with this experimental observation.
Introduction to the 30th volume of Inverse Problems
NASA Astrophysics Data System (ADS)
Louis, Alfred K.
2014-01-01
The field of inverse problems is a fast-developing domain of research originating from the practical demands of finding the cause when a result is observed. The woodpecker, searching for insects, is probing a tree using sound waves: the information searched for is whether there is an insect or not, hence a 0-1 decision. When the result has to contain more information, ad hoc solutions are not at hand and more sophisticated methods have to be developed. Right from its first appearance, the field of inverse problems has been characterized by an interdisciplinary nature: the interpretation of measured data, reinforced by mathematical models serving the analyzing questions of observability, stability and resolution, developing efficient, stable and accurate algorithms to gain as much information as possible from the input and to feedback to the questions of optimal measurement configuration. As is typical for a new area of research, facets of it are separated and studied independently. Hence, fields such as the theory of inverse scattering, tomography in general and regularization methods have developed. However, all aspects have to be reassembled to arrive at the best possible solution to the problem at hand. This development is reflected by the first and still leading journal in the field, Inverse Problems. Founded by pioneers Roy Pike from London and Pierre Sabatier from Montpellier, who enjoyably describes the journal's nascence in his book Rêves et Combats d'un Enseignant-Chercheur, Retour Inverse [1], the journal has developed successfully over the last few decades. Neither the Editors-in-Chief, formerly called Honorary Editors, nor the board or authors could have set the path to success alone. Their fruitful interplay, complemented by the efficient and highly competent publishing team at IOP Publishing, has been fundamental. As such it is my honor and pleasure to follow my renowned colleagues Pierre Sabatier, Mario Bertero, Frank Natterer, Alberto Grünbaum and
Negative Compressibility and Inverse Problem for Spinning Gas
Vasily Geyko and Nathaniel J. Fisch
2013-01-11
A spinning ideal gas in a cylinder with a smooth surface is shown to have unusual properties. First, under compression parallel to the axis of rotation, the spinning gas exhibits negative compressibility because energy can be stored in the rotation. Second, the spinning breaks the symmetry under which partial pressures of a mixture of gases simply add proportional to the constituent number densities. Thus, remarkably, in a mixture of spinning gases, an inverse problem can be formulated such that the gas constituents can be determined through external measurements only.
On Lambda and Time Operators: the Inverse Intertwining Problem Revisited
NASA Astrophysics Data System (ADS)
Gómez-Cubillo, F.; Suchanecki, Z.; Villullas, S.
2011-07-01
An exact theory of irreversibility was proposed by Misra, Prigogine and Courbage, based on non-unitary similarity transformations Λ that intertwine reversible dynamics and irreversible ones. This would advocate the idea that irreversible behavior would originate at the microscopic level. Reversible evolution with an internal time operator have the intertwining property. Recently the inverse intertwining problem has been answered in the negative, that is, not every unitary evolution allowing such Λ-transformation has an internal time. This work contributes new results in this direction.
An analytic method for the inverse problem of MREPT
NASA Astrophysics Data System (ADS)
Palamodov, V.
2016-03-01
Magnetic resonance electric properties tomography (MREPT) is a medical imaging modality for visualizing the electrical tissue properties of the human body using radio-frequency magnetic fields. This method consists of reconstructing the admittivity distribution from the positive rotating component of the magnetic field. In the newest paper of Ammari et al (2015 Inverse Problems 31 105001) an approximate method of reconstruction of variable admittivity was proposed. In this paper a method for exact reconstruction of the admittivity from data of the positive rotating component of the field is given.
A verifiable solution to the MEG inverse problem.
Barnes, Gareth R; Furlong, Paul L; Singh, Krish D; Hillebrand, Arjan
2006-06-01
Magnetoencephalography (MEG) is a non-invasive brain imaging technique with the potential for very high temporal and spatial resolution of neuronal activity. The main stumbling block for the technique has been that the estimation of a neuronal current distribution, based on sensor data outside the head, is an inverse problem with an infinity of possible solutions. Many inversion techniques exist, all using different a-priori assumptions in order to reduce the number of possible solutions. Although all techniques can be thoroughly tested in simulation, implicit in the simulations are the experimenter's own assumptions about realistic brain function. To date, the only way to test the validity of inversions based on real MEG data has been through direct surgical validation, or through comparison with invasive primate data. In this work, we constructed a null hypothesis that the reconstruction of neuronal activity contains no information on the distribution of the cortical grey matter. To test this, we repeatedly compared rotated sections of grey matter with a beamformer estimate of neuronal activity to generate a distribution of mutual information values. The significance of the comparison between the un-rotated anatomical information and the electrical estimate was subsequently assessed against this distribution. We found that there was significant (P < 0.05) anatomical information contained in the beamformer images across a number of frequency bands. Based on the limited data presented here, we can say that the assumptions behind the beamformer algorithm are not unreasonable for the visual-motor task investigated. PMID:16480896
A spatiotemporal dynamic distributed solution to the MEG inverse problem.
Lamus, Camilo; Hämäläinen, Matti S; Temereanca, Simona; Brown, Emery N; Purdon, Patrick L
2012-11-01
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic maximum a posteriori expectation-maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods. PMID:22155043
A spatiotemporal dynamic distributed solution to the MEG inverse problem
Lamus, Camilo; Hämäläinen, Matti S.; Temereanca, Simona; Brown, Emery N.; Purdon, Patrick L.
2012-01-01
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-conditioned inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be improved by incorporating models of these dynamics. In this article, we present a model for cortical activity based on nearest-neighbor autoregression that incorporates local spatiotemporal interactions between distributed sources in a manner consistent with neurophysiology and neuroanatomy. We develop a dynamic Maximum a Posteriori Expectation-Maximization (dMAP-EM) source localization algorithm for estimation of cortical sources and model parameters based on the Kalman Filter, the Fixed Interval Smoother, and the EM algorithms. We apply the dMAP-EM algorithm to simulated experiments as well as to human experimental data. Furthermore, we derive expressions to relate our dynamic estimation formulas to those of standard static models, and show how dynamic methods optimally assimilate past and future data. Our results establish the feasibility of spatiotemporal dynamic estimation in large-scale distributed source spaces with several thousand source locations and hundreds of sensors, with resulting inverse solutions that provide substantial performance improvements over static methods. PMID:22155043
Inverse problem of quadratic time-dependent Hamiltonians
NASA Astrophysics Data System (ADS)
Guo, Guang-Jie; Meng, Yan; Chang, Hong; Duan, Hui-Zeng; Di, Bing
2015-08-01
Using an algebraic approach, it is possible to obtain the temporal evolution wave function for a Gaussian wave-packet obeying the quadratic time-dependent Hamiltonian (QTDH). However, in general, most of the practical cases are not exactly solvable, for we need general solutions of the Riccatti equations which are not generally known. We therefore bypass directly solving for the temporal evolution wave function, and study its inverse problem. We start with a particular evolution of the wave-packet, and get the required Hamiltonian by using the inverse method. The inverse approach opens up a new way to find new exact solutions to the QTDH. Some typical examples are studied in detail. For a specific time-dependent periodic harmonic oscillator, the Berry phase is obtained exactly. Project supported by the National Natural Science Foundation of China (Grant No. 11347171), the Natural Science Foundation of Hebei Province of China (Grant No. A2012108003), and the Key Project of Educational Commission of Hebei Province of China (Grant No. ZD2014052).
Evaluation of simplified evaporation duct refractivity models for inversion problems
NASA Astrophysics Data System (ADS)
Saeger, J. T.; Grimes, N. G.; Rickard, H. E.; Hackett, E. E.
2015-10-01
To assess a radar system's instantaneous performance on any given day, detailed knowledge of the meteorological conditions is required due to the dependency of atmospheric refractivity on thermodynamic properties such as temperature, water vapor, and pressure. Because of the significant challenges involved in obtaining these data, recent efforts have focused on development of methods to obtain the refractivity structure inversely using radar measurements and radar wave propagation models. Such inversion techniques generally use simplified refractivity models in order to reduce the parameter space of the solution. Here the accuracy of three simple refractivity models is examined for the case of an evaporation duct. The models utilize the basic log linear shape classically associated with evaporation ducts, but each model depends on various parameters that affect different aspects of the profile, such as its shape and duct height. The model parameters are optimized using radiosonde data, and their performance is compared to these atmospheric measurements. The optimized models and data are also used to predict propagation using a parabolic equation code with the refractivity prescribed by the models and measured data, and the resulting propagation patterns are compared. The results of this study suggest that the best log linear model formulation for an inversion problem would be a two-layer model that contains at least three parameters: duct height, duct curvature, and mixed layer slope. This functional form permits a reasonably accurate fit to atmospheric measurements as well as embodies key features of the profile required for correct propagation prediction with as few parameters as possible.
Topological inversion for solution of geodesy-constrained geophysical problems
NASA Astrophysics Data System (ADS)
Saltogianni, Vasso; Stiros, Stathis
2015-04-01
Geodetic data, mostly GPS observations, permit to measure displacements of selected points around activated faults and volcanoes, and on the basis of geophysical models, to model the underlying physical processes. This requires inversion of redundant systems of highly non-linear equations with >3 unknowns; a situation analogous to the adjustment of geodetic networks. However, in geophysical problems inversion cannot be based on conventional least-squares techniques, and is based on numerical inversion techniques (a priori fixing of some variables, optimization in steps with values of two variables each time to be regarded fixed, random search in the vicinity of approximate solutions). Still these techniques lead to solutions trapped in local minima, to correlated estimates and to solutions with poor error control (usually sampling-based approaches). To overcome these problems, a numerical-topological, grid-search based technique in the RN space is proposed (N the number of unknown variables). This technique is in fact a generalization and refinement of techniques used in lighthouse positioning and in some cases of low-accuracy 2-D positioning using Wi-Fi etc. The basic concept is to assume discrete possible ranges of each variable, and from these ranges to define a grid G in the RN space, with some of the gridpoints to approximate the true solutions of the system. Each point of hyper-grid G is then tested whether it satisfies the observations, given their uncertainty level, and successful grid points define a sub-space of G containing the true solutions. The optimal (minimal) space containing one or more solutions is obtained using a trial-and-error approach, and a single optimization factor. From this essentially deterministic identification of the set of gridpoints satisfying the system of equations, at a following step, a stochastic optimal solution is computed corresponding to the center of gravity of this set of gridpoints. This solution corresponds to a
Inverse Problems in Complex Models and Applications to Earth Sciences
NASA Astrophysics Data System (ADS)
Bosch, M. E.
2015-12-01
The inference of the subsurface earth structure and properties requires the integration of different types of data, information and knowledge, by combined processes of analysis and synthesis. To support the process of integrating information, the regular concept of data inversion is evolving to expand its application to models with multiple inner components (properties, scales, structural parameters) that explain multiple data (geophysical survey data, well-logs, core data). The probabilistic inference methods provide the natural framework for the formulation of these problems, considering a posterior probability density function (PDF) that combines the information from a prior information PDF and the new sets of observations. To formulate the posterior PDF in the context of multiple datasets, the data likelihood functions are factorized assuming independence of uncertainties for data originating across different surveys. A realistic description of the earth medium requires modeling several properties and structural parameters, which relate to each other according to dependency and independency notions. Thus, conditional probabilities across model components also factorize. A common setting proceeds by structuring the model parameter space in hierarchical layers. A primary layer (e.g. lithology) conditions a secondary layer (e.g. physical medium properties), which conditions a third layer (e.g. geophysical data). In general, less structured relations within model components and data emerge from the analysis of other inverse problems. They can be described with flexibility via direct acyclic graphs, which are graphs that map dependency relations between the model components. Examples of inverse problems in complex models can be shown at various scales. At local scale, for example, the distribution of gas saturation is inferred from pre-stack seismic data and a calibrated rock-physics model. At regional scale, joint inversion of gravity and magnetic data is applied
Review on solving the inverse problem in EEG source analysis
Grech, Roberta; Cassar, Tracey; Muscat, Joseph; Camilleri, Kenneth P; Fabri, Simon G; Zervakis, Michalis; Xanthopoulos, Petros; Sakkalis, Vangelis; Vanrumste, Bart
2008-01-01
In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels
Flexible hydraulic DFN for direct and inverse flow problems
NASA Astrophysics Data System (ADS)
de Dreuzy, J.; Le Goc, R.; Davy, P.; Bour, O.
2006-12-01
Natural fractured media are complex because of the large number of fractures of widely-scattered characteristics (length, transmissivity). Simulating hydraulic processes by accounting for the natural complexity requires flexible software and high performance computing. We develop a parallel fracture network software MPFRAC (for Massive Parallel FRACture network) designed to handle 2D and 3D large networks with fractures of different shapes (ellipses and polygons) delimited by a polyhedron. MPFRAC contains a broad range of fracture generation possibilities to which can be easily added other methods. In 3D, a specific module creates a consistent meshing of the fractures matching at the fracture intersections. The quality of the meshing required for finite element methods is ensured by projecting and the discretizing fracture intersections on a regular grid. Flow is computed on the meshed structure by a mixed finite element scheme and the yielded system can be solved using parallel solvers. Particle transport is subsequently simulated by a particle transport method. All modules of MPFRAC are independent and parallel enhancing the software flexibility and performances. High performance computing on PC-clusters is especially required for 3D flow and transport simulations. We use this software first for direct simulation to determine the influence of the fracture characteristics on the equivalent permeability, flow structure and pump test responses. Based on stochastic fracture distribution characteristics (fracture length, transmissivity, orientation and position distributions) Monte-Carlo simulations are performed in order to extract equivalent simplified flow models conditioned by a very limited number of parameters that could be taken as the a priori reduced search space of the inverse problem. Secondly, we use it for inverse problems currently in 2D for studying the influence of the data density on the identification of a limited number of fracture characteristics
Regularized total least squares approach for nonconvolutional linear inverse problems.
Zhu, W; Wang, Y; Galatsanos, N P; Zhang, J
1999-01-01
In this correspondence, a solution is developed for the regularized total least squares (RTLS) estimate in linear inverse problems where the linear operator is nonconvolutional. Our approach is based on a Rayleigh quotient (RQ) formulation of the TLS problem, and we accomplish regularization by modifying the RQ function to enforce a smooth solution. A conjugate gradient algorithm is used to minimize the modified RQ function. As an example, the proposed approach has been applied to the perturbation equation encountered in optical tomography. Simulation results show that this method provides more stable and accurate solutions than the regularized least squares and a previously reported total least squares approach, also based on the RQ formulation. PMID:18267442
Galerkin approximation for inverse problems for nonautonomous nonlinear distributed systems
NASA Technical Reports Server (NTRS)
Banks, H. T.; Reich, Simeon; Rosen, I. G.
1988-01-01
An abstract framework and convergence theory is developed for Galerkin approximation for inverse problems involving the identification of nonautonomous nonlinear distributed parameter systems. A set of relatively easily verified conditions is provided which are sufficient to guarantee the existence of optimal solutions and their approximation by a sequence of solutions to a sequence of approximating finite dimensional identification problems. The approach is based on the theory of monotone operators in Banach spaces and is applicable to a reasonably broad class of nonlinear distributed systems. Operator theoretic and variational techniques are used to establish a fundamental convergence result. An example involving evolution systems with dynamics described by nonstationary quasilinear elliptic operators along with some applications are presented and discussed.
Inverse problems and computational cell metabolic models: a statistical approach
NASA Astrophysics Data System (ADS)
Calvetti, D.; Somersalo, E.
2008-07-01
In this article, we give an overview of the Bayesian modelling of metabolic systems at the cellular and subcellular level. The models are based on detailed description of key biochemical reactions occurring in tissue, which may in turn be compartmentalized into cytosol and mitochondria, and of transports between the compartments. The classical deterministic approach which models metabolic systems as dynamical systems with Michaelis-Menten kinetics, is replaced by a stochastic extension where the model parameters are interpreted as random variables with an appropriate probability density. The inverse problem of cell metabolism in this setting consists of estimating the density of the model parameters. After discussing some possible approaches to solving the problem, we address the issue of how to assess the reliability of the predictions of a stochastic model by proposing an output analysis in terms of model uncertainties. Visualization modalities for organizing the large amount of information provided by the Bayesian dynamic sensitivity analysis are also illustrated.
Inverse spin glass and related maximum entropy problems.
Castellana, Michele; Bialek, William
2014-09-12
If we have a system of binary variables and we measure the pairwise correlations among these variables, then the least structured or maximum entropy model for their joint distribution is an Ising model with pairwise interactions among the spins. Here we consider inhomogeneous systems in which we constrain, for example, not the full matrix of correlations, but only the distribution from which these correlations are drawn. In this sense, what we have constructed is an inverse spin glass: rather than choosing coupling constants at random from a distribution and calculating correlations, we choose the correlations from a distribution and infer the coupling constants. We argue that such models generate a block structure in the space of couplings, which provides an explicit solution of the inverse problem. This allows us to generate a phase diagram in the space of (measurable) moments of the distribution of correlations. We expect that these ideas will be most useful in building models for systems that are nonequilibrium statistical mechanics problems, such as networks of real neurons. PMID:25260004
Fisher information for inverse problems and trace class operators
NASA Astrophysics Data System (ADS)
Nordebo, S.; Gustafsson, M.; Khrennikov, A.; Nilsson, B.; Toft, J.
2012-12-01
This paper provides a mathematical framework for Fisher information analysis for inverse problems based on Gaussian noise on infinite-dimensional Hilbert space. The covariance operator for the Gaussian noise is assumed to be trace class, and the Jacobian of the forward operator Hilbert-Schmidt. We show that the appropriate space for defining the Fisher information is given by the Cameron-Martin space. This is mainly because the range space of the covariance operator always is strictly smaller than the Hilbert space. For the Fisher information to be well-defined, it is furthermore required that the range space of the Jacobian is contained in the Cameron-Martin space. In order for this condition to hold and for the Fisher information to be trace class, a sufficient condition is formulated based on the singular values of the Jacobian as well as of the eigenvalues of the covariance operator, together with some regularity assumptions regarding their relative rate of convergence. An explicit example is given regarding an electromagnetic inverse source problem with "external" spherically isotropic noise, as well as "internal" additive uncorrelated noise.
Comparison of optimal design methods in inverse problems
NASA Astrophysics Data System (ADS)
Banks, H. T.; Holm, K.; Kappel, F.
2011-07-01
Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).
Inverse zombies, anesthesia awareness, and the hard problem of unconsciousness.
Mashour, George A; LaRock, Eric
2008-12-01
Philosophical (p-) zombies are constructs that possess all of the behavioral features and responses of a sentient human being, yet are not conscious. P-zombies are intimately linked to the hard problem of consciousness and have been invoked as arguments against physicalist approaches. But what if we were to invert the characteristics of p-zombies? Such an inverse (i-) zombie would possess all of the behavioral features and responses of an insensate being, yet would nonetheless be conscious. While p-zombies are logically possible but naturally improbable, an approximation of i-zombies actually exists: individuals experiencing what is referred to as "anesthesia awareness." Patients under general anesthesia may be intubated (preventing speech), paralyzed (preventing movement), and narcotized (minimizing response to nociceptive stimuli). Thus, they appear--and typically are--unconscious. In 1-2 cases/1000, however, patients may be aware of intraoperative events, sometimes without any objective indices. Furthermore, a much higher percentage of patients (22% in a recent study) may have the subjective experience of dreaming during general anesthesia. P-zombies confront us with the hard problem of consciousness--how do we explain the presence of qualia? I-zombies present a more practical problem--how do we detect the presence of qualia? The current investigation compares p-zombies to i-zombies and explores the "hard problem" of unconsciousness with a focus on anesthesia awareness. PMID:18635380
Basis set expansion for inverse problems in plasma diagnostic analysis.
Jones, B; Ruiz, C L
2013-07-01
A basis set expansion method [V. Dribinski, A. Ossadtchi, V. A. Mandelshtam, and H. Reisler, Rev. Sci. Instrum. 73, 2634 (2002)] is applied to recover physical information about plasma radiation sources from instrument data, which has been forward transformed due to the nature of the measurement technique. This method provides a general approach for inverse problems, and we discuss two specific examples relevant to diagnosing fast z pinches on the 20-25 MA Z machine [M. E. Savage, L. F. Bennett, D. E. Bliss, W. T. Clark, R. S. Coats, J. M. Elizondo, K. R. LeChien, H. C. Harjes, J. M. Lehr, J. E. Maenchen, D. H. McDaniel, M. F. Pasik, T. D. Pointon, A. C. Owen, D. B. Seidel, D. L. Smith, B. S. Stoltzfus, K. W. Struve, W. A. Stygar, L. K. Warne, J. R. Woodworth, C. W. Mendel, K. R. Prestwich, R. W. Shoup, D. L. Johnson, J. P. Corley, K. C. Hodge, T. C. Wagoner, and P. E. Wakeland, in Proceedings of the Pulsed Power Plasma Sciences Conference (IEEE, 2007), p. 979]. First, Abel inversion of time-gated, self-emission x-ray images from a wire array implosion is studied. Second, we present an approach for unfolding neutron time-of-flight measurements from a deuterium gas puff z pinch to recover information about emission time history and energy distribution. Through these examples, we discuss how noise in the measured data limits the practical resolution of the inversion, and how the method handles discontinuities in the source function and artifacts in the projected image. We add to the method a propagation of errors calculation for estimating uncertainties in the inverted solution. PMID:23902066
Basis set expansion for inverse problems in plasma diagnostic analysis
NASA Astrophysics Data System (ADS)
Jones, B.; Ruiz, C. L.
2013-07-01
A basis set expansion method [V. Dribinski, A. Ossadtchi, V. A. Mandelshtam, and H. Reisler, Rev. Sci. Instrum. 73, 2634 (2002)], 10.1063/1.1482156 is applied to recover physical information about plasma radiation sources from instrument data, which has been forward transformed due to the nature of the measurement technique. This method provides a general approach for inverse problems, and we discuss two specific examples relevant to diagnosing fast z pinches on the 20-25 MA Z machine [M. E. Savage, L. F. Bennett, D. E. Bliss, W. T. Clark, R. S. Coats, J. M. Elizondo, K. R. LeChien, H. C. Harjes, J. M. Lehr, J. E. Maenchen, D. H. McDaniel, M. F. Pasik, T. D. Pointon, A. C. Owen, D. B. Seidel, D. L. Smith, B. S. Stoltzfus, K. W. Struve, W. A. Stygar, L. K. Warne, J. R. Woodworth, C. W. Mendel, K. R. Prestwich, R. W. Shoup, D. L. Johnson, J. P. Corley, K. C. Hodge, T. C. Wagoner, and P. E. Wakeland, in Proceedings of the Pulsed Power Plasma Sciences Conference (IEEE, 2007), p. 979]. First, Abel inversion of time-gated, self-emission x-ray images from a wire array implosion is studied. Second, we present an approach for unfolding neutron time-of-flight measurements from a deuterium gas puff z pinch to recover information about emission time history and energy distribution. Through these examples, we discuss how noise in the measured data limits the practical resolution of the inversion, and how the method handles discontinuities in the source function and artifacts in the projected image. We add to the method a propagation of errors calculation for estimating uncertainties in the inverted solution.
Basis set expansion for inverse problems in plasma diagnostic analysis
Jones, B.; Ruiz, C. L.
2013-07-15
A basis set expansion method [V. Dribinski, A. Ossadtchi, V. A. Mandelshtam, and H. Reisler, Rev. Sci. Instrum. 73, 2634 (2002)] is applied to recover physical information about plasma radiation sources from instrument data, which has been forward transformed due to the nature of the measurement technique. This method provides a general approach for inverse problems, and we discuss two specific examples relevant to diagnosing fast z pinches on the 20–25 MA Z machine [M. E. Savage, L. F. Bennett, D. E. Bliss, W. T. Clark, R. S. Coats, J. M. Elizondo, K. R. LeChien, H. C. Harjes, J. M. Lehr, J. E. Maenchen, D. H. McDaniel, M. F. Pasik, T. D. Pointon, A. C. Owen, D. B. Seidel, D. L. Smith, B. S. Stoltzfus, K. W. Struve, W. A. Stygar, L. K. Warne, J. R. Woodworth, C. W. Mendel, K. R. Prestwich, R. W. Shoup, D. L. Johnson, J. P. Corley, K. C. Hodge, T. C. Wagoner, and P. E. Wakeland, in Proceedings of the Pulsed Power Plasma Sciences Conference (IEEE, 2007), p. 979]. First, Abel inversion of time-gated, self-emission x-ray images from a wire array implosion is studied. Second, we present an approach for unfolding neutron time-of-flight measurements from a deuterium gas puff z pinch to recover information about emission time history and energy distribution. Through these examples, we discuss how noise in the measured data limits the practical resolution of the inversion, and how the method handles discontinuities in the source function and artifacts in the projected image. We add to the method a propagation of errors calculation for estimating uncertainties in the inverted solution.
Theoretical study of Laplacian electrocardiography forward and inverse problem
NASA Astrophysics Data System (ADS)
Wu, Dongsheng
The present study is concerned with a fundamental problem of cardiac electrophysiology, that is relating in a quantitative way the electrical activity within the heart to the signals recorded over the body surface. By computer simulation, a rigorous evaluation of the performance of the body surface Laplacian electrocardiographic maps in a physiologically reasonable and well-controlled computational setting is provided in this dissertation. The present forward heart-torso model, a three-dimensional ventricular conduction model embedded in a realistically shaped inhomogeneous torso volume conductor model, represents, up to date, the most advanced computer model, which is available for studying the Laplacian electrocardiographic fields corresponding to normal and abnormal ventricular conduction processes. Theoretical studies show that one can achieve enhanced spatial resolution of mapping cardiac electrical activity by obtaining the Laplacian ECG over the body surface. The present work demonstrates the excellent performance of the body surface Laplacian electrocardiographic maps in resolving and imaging the underlying regional myocardial electrical activity. The biophysics underlying this is that the Laplacian ECG heavily weights the contributions from the myocardial bioelectric sources that are closest to the recording location, whereas the potential ECG sums up the contributions from a large area of activated myocardial tissue. It is this regional nature of the Laplacian ECG that makes it possible to provide a more localized body surface manifestation of the underlying regional myocardial electrical activity. The feasibility of applying the Laplacian ECG to the inverse problems has also been investigated. Theoretical studies of the Laplacian electrocardiogram based inverse problem by using a homogeneous spherical volume conductor and a realistically shaped volume conductor have been conducted. The present work shows encouraging results which suggest the feasibility
NASA Technical Reports Server (NTRS)
Backus, George
1987-01-01
Let R be the real numbers, R(n) the linear space of all real n-tuples, and R(infinity) the linear space of all infinite real sequences x = (x sub 1, x sub 2,...). Let P sub n :R(infinity) approaches R(n) be the projection operator with P sub n (x) = (x sub 1,...,x sub n). Let p(infinity) be a probability measure on the smallest sigma-ring of subsets of R(infinity) which includes all of the cylinder sets P sub n(-1) (B sub n), where B sub n is an arbitrary Borel subset of R(n). Let p sub n be the marginal distribution of p(infinity) on R(n), so p sub n(B sub n) = p(infinity)(P sub n to the -1(B sub n)) for each B sub n. A measure on R(n) is isotropic if it is invariant under all orthogonal transformations of R(n). All members of the set of all isotropic probability distributions on R(n) are described. The result calls into question both stochastic inversion and Bayesian inference, as currently used in many geophysical inverse problems.
Solution accelerators for large scale 3D electromagnetic inverse problems
Newman, Gregory A.; Boggs, Paul T.
2004-04-05
We provide a framework for preconditioning nonlinear 3D electromagnetic inverse scattering problems using nonlinear conjugate gradient (NLCG) and limited memory (LM) quasi-Newton methods. Key to our approach is the use of an approximate adjoint method that allows for an economical approximation of the Hessian that is updated at each inversion iteration. Using this approximate Hessian as a preconditoner, we show that the preconditioned NLCG iteration converges significantly faster than the non-preconditioned iteration, as well as converging to a data misfit level below that observed for the non-preconditioned method. Similar conclusions are also observed for the LM iteration; preconditioned with the approximate Hessian, the LM iteration converges faster than the non-preconditioned version. At this time, however, we see little difference between the convergence performance of the preconditioned LM scheme and the preconditioned NLCG scheme. A possible reason for this outcome is the behavior of the line search within the LM iteration. It was anticipated that, near convergence, a step size of one would be approached, but what was observed, instead, were step lengths that were nowhere near one. We provide some insights into the reasons for this behavior and suggest further research that may improve the performance of the LM methods.
Inverse problems in heterogeneous and fractured media using peridynamics
Turner, Daniel Z.; van Bloemen Waanders, Bart G.; Parks, Michael L.
2015-12-10
The following work presents an adjoint-based methodology for solving inverse problems in heterogeneous and fractured media using state-based peridynamics. We show that the inner product involving the peridynamic operators is self-adjoint. The proposed method is illustrated for several numerical examples with constant and spatially varying material parameters as well as in the context of fractures. We also present a framework for obtaining material parameters by integrating digital image correlation (DIC) with inverse analysis. This framework is demonstrated by evaluating the bulk and shear moduli for a sample of nuclear graphite using digital photographs taken during the experiment. The resulting measuredmore » values correspond well with other results reported in the literature. Lastly, we show that this framework can be used to determine the load state given observed measurements of a crack opening. Furthermore, this type of analysis has many applications in characterizing subsurface stress-state conditions given fracture patterns in cores of geologic material.« less
The LHC Inverse Problem, Supersymmetry and the ILC
Berger, C.F.; Gainer, J.S.; Hewett, J.L.; Lillie, B.; Rizzo, T.G.
2007-11-12
We address the question whether the ILC can resolve the LHC Inverse Problem within the framework of the MSSM. We examine 242 points in the MSSM parameter space which were generated at random and were found to give indistinguishable signatures at the LHC. After a realistic simulation including full Standard Model backgrounds and a fast detector simulation, we find that roughly only one third of these scenarios lead to visible signatures of some kind with a significance {ge} 5 at the ILC with {radical}s = 500 GeV. Furthermore, we examine these points in parameter space pairwise and find that only one third of the pairs are distinguishable at the ILC at 5{sigma}.
Multi-scale Plasmonic Nanoparticles and the Inverse Problem
Odom, Teri W.; You, Eun-Ah; Sweeney, Christina M.
2012-01-01
This Perspective describes how multi-scale plasmonic structures with two or more length scales (fine, medium, coarse) provide an experimental route for addressing the inverse problem. Specific near-field and far-field optical properties can be targeted and compiled into a plasmon resonance library by taking advantage of length scales spanning three orders of magnitude, from 1 nm to greater than 1000 nm, in a single particle. Examples of multi-scale 1D, 2D, and 3D gold structures created by nanofabrication tools and templates are discussed, and unexpected optical properties compared to those from their smaller counterparts are emphasized. One application of multi-scale particle dimers for surface-enhanced Raman spectroscopy is also described. PMID:23066451
Geomagnetic inverse problem and data assimilation: a progress report
NASA Astrophysics Data System (ADS)
Aubert, Julien; Fournier, Alexandre
2013-04-01
In this presentation I will present two studies recently undertaken by our group in an effort to bring the benefits of data assimilation to the study of Earth's magnetic field and the dynamics of its liquid iron core, where the geodynamo operates. In a first part I will focus on the geomagnetic inverse problem, which attempts to recover the fluid flow in the core from the temporal variation of the magnetic field (known as the secular variation). Geomagnetic data can be downward continued from the surface of the Earth down to the core-mantle boundary, but not further below, since the core is an electrical conductor. Historically, solutions to the geomagnetic inverse problem in such a sparsely observed system were thus found only for flow immediately below the core mantle boundary. We have recently shown that combining a numerical model of the geodynamo together with magnetic observations, through the use of Kalman filtering, now allows to present solutions for flow throughout the core. In a second part, I will present synthetic tests of sequential geomagnetic data assimilation aiming at evaluating the range at which the future of the geodynamo can be predicted, and our corresponding prospects to refine the current geomagnetic predictions. Fournier, Aubert, Thébault: Inference on core surface flow from observations and 3-D dynamo modelling, Geophys. J. Int. 186, 118-136, 2011, doi: 10.1111/j.1365-246X.2011.05037.x Aubert, Fournier: Inferring internal properties of Earth's core dynamics and their evolution from surface observations and a numerical geodynamo model, Nonlinear Proc. Geoph. 18, 657-674, 2011, doi:10.5194/npg-18-657-2011 Aubert: Flow throughout the Earth's core inverted from geomagnetic observations and numerical dynamo models, Geophys. J. Int., 2012, doi: 10.1093/gji/ggs051
Nonlocal regularization of inverse problems: a unified variational framework.
Yang, Zhili; Jacob, Mathews
2013-08-01
We introduce a unifying energy minimization framework for nonlocal regularization of inverse problems. In contrast to the weighted sum of square differences between image pixels used by current schemes, the proposed functional is an unweighted sum of inter-patch distances. We use robust distance metrics that promote the averaging of similar patches, while discouraging the averaging of dissimilar patches. We show that the first iteration of a majorize-minimize algorithm to minimize the proposed cost function is similar to current nonlocal methods. The reformulation thus provides a theoretical justification for the heuristic approach of iterating nonlocal schemes, which re-estimate the weights from the current image estimate. Thanks to the reformulation, we now understand that the widely reported alias amplification associated with iterative nonlocal methods are caused by the convergence to local minimum of the nonconvex penalty. We introduce an efficient continuation strategy to overcome this problem. The similarity of the proposed criterion to widely used nonquadratic penalties (e.g., total variation and lp semi-norms) opens the door to the adaptation of fast algorithms developed in the context of compressive sensing; we introduce several novel algorithms to solve the proposed nonlocal optimization problem. Thanks to the unifying framework, these fast algorithms are readily applicable for a large class of distance metrics. PMID:23014745
Nonlocal regularization of inverse problems: a unified variational framework
Yang, Zhili; Jacob, Mathews
2014-01-01
We introduce a unifying energy minimization framework for nonlocal regularization of inverse problems. In contrast to the weighted sum of square differences between image pixels used by current schemes, the proposed functional is an unweighted sum of inter-patch distances. We use robust distance metrics that promote the averaging of similar patches, while discouraging the averaging of dissimilar patches. We show that the first iteration of a majorize-minimize algorithm to minimize the proposed cost function is similar to current non-local methods. The reformulation thus provides a theoretical justification for the heuristic approach of iterating non-local schemes, which re-estimate the weights from the current image estimate. Thanks to the reformulation, we now understand that the widely reported alias amplification associated with iterative non-local methods are caused by the convergence to local minimum of the nonconvex penalty. We introduce an efficient continuation strategy to overcome this problem. The similarity of the proposed criterion to widely used non-quadratic penalties (eg. total variation and `p semi-norms) opens the door to the adaptation of fast algorithms developed in the context of compressive sensing; we introduce several novel algorithms to solve the proposed non-local optimization problem. Thanks to the unifying framework, these fast algorithms are readily applicable for a large class of distance metrics. PMID:23014745
Solution of the inverse problem of magnetic induction tomography (MIT).
Merwa, Robert; Hollaus, Karl; Brunner, Patricia; Scharfetter, Hermann
2005-04-01
Magnetic induction tomography (MIT) of biological tissue is used to reconstruct the changes in the complex conductivity distribution inside an object under investigation. The measurement principle is based on determining the perturbation DeltaB of a primary alternating magnetic field B0, which is coupled from an array of excitation coils to the object under investigation. The corresponding voltages DeltaV and V0 induced in a receiver coil carry the information about the passive electrical properties (i.e. conductivity, permittivity and permeability). The reconstruction of the conductivity distribution requires the solution of a 3D inverse eddy current problem. As in EIT the inverse problem is ill-posed and on this account some regularization scheme has to be applied. We developed an inverse solver based on the Gauss-Newton-one-step method for differential imaging, and we implemented and tested four different regularization schemes: the first and second approaches employ a classical smoothness criterion using the unit matrix and a differential matrix of first order as the regularization matrix. The third method is based on variance uniformization, and the fourth method is based on the truncated singular value decomposition. Reconstructions were carried out with synthetic measurement data generated with a spherical perturbation at different locations within a conducting cylinder. Data were generated on a different mesh and 1% random noise was added. The model contained 16 excitation coils and 32 receiver coils which could be combined pairwise to give 16 planar gradiometers. With 32 receiver coils all regularization methods yield fairly good 3D-images of the modelled changes of the conductivity distribution, and prove the feasibility of difference imaging with MIT. The reconstructed perturbations appear at the right location, and their size is in the expected range. With 16 planar gradiometers an additional spurious feature appears mirrored with respect to the median
The geometry of discombinations and its applications to semi-inverse problems in anelasticity.
Yavari, Arash; Goriely, Alain
2014-09-01
The geometrical formulation of continuum mechanics provides us with a powerful approach to understand and solve problems in anelasticity where an elastic deformation is combined with a non-elastic component arising from defects, thermal stresses, growth effects or other effects leading to residual stresses. The central idea is to assume that the material manifold, prescribing the reference configuration for a body, has an intrinsic, non-Euclidean, geometrical structure. Residual stresses then naturally arise when this configuration is mapped into Euclidean space. Here, we consider the problem of discombinations (a new term that we introduce in this paper), that is, a combined distribution of fields of dislocations, disclinations and point defects. Given a discombination, we compute the geometrical characteristics of the material manifold (curvature, torsion, non-metricity), its Cartan's moving frames and structural equations. This identification provides a powerful algorithm to solve semi-inverse problems with non-elastic components. As an example, we calculate the residual stress field of a cylindrically symmetric distribution of discombinations in an infinite circular cylindrical bar made of an incompressible hyperelastic isotropic elastic solid. PMID:25197257
The geometry of discombinations and its applications to semi-inverse problems in anelasticity
Yavari, Arash; Goriely, Alain
2014-01-01
The geometrical formulation of continuum mechanics provides us with a powerful approach to understand and solve problems in anelasticity where an elastic deformation is combined with a non-elastic component arising from defects, thermal stresses, growth effects or other effects leading to residual stresses. The central idea is to assume that the material manifold, prescribing the reference configuration for a body, has an intrinsic, non-Euclidean, geometrical structure. Residual stresses then naturally arise when this configuration is mapped into Euclidean space. Here, we consider the problem of discombinations (a new term that we introduce in this paper), that is, a combined distribution of fields of dislocations, disclinations and point defects. Given a discombination, we compute the geometrical characteristics of the material manifold (curvature, torsion, non-metricity), its Cartan's moving frames and structural equations. This identification provides a powerful algorithm to solve semi-inverse problems with non-elastic components. As an example, we calculate the residual stress field of a cylindrically symmetric distribution of discombinations in an infinite circular cylindrical bar made of an incompressible hyperelastic isotropic elastic solid. PMID:25197257
A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems
NASA Astrophysics Data System (ADS)
Iglesias, Marco A.
2016-02-01
. The numerical investigation is carried out with synthetic experiments on two model inverse problems: (i) identification of conductivity on a Darcy flow model and (ii) electrical impedance tomography with the complete electrode model. We further demonstrate the potential application of the method in solving shape identification problems that arises from the aforementioned forward models by means of a level-set approach for the parameterization of unknown geometries.
Methodes entropiques appliquees au probleme inverse en magnetoencephalographie
NASA Astrophysics Data System (ADS)
Lapalme, Ervig
2005-07-01
This thesis is devoted to biomagnetic source localization using magnetoencephalography. This problem is known to have an infinite number of solutions. So methods are required to take into account anatomical and functional information on the solution. The work presented in this thesis uses the maximum entropy on the mean method to constrain the solution. This method originates from statistical mechanics and information theory. This thesis is divided into two main parts containing three chapters each. The first part reviews the magnetoencephalographic inverse problem: the theory needed to understand its context and the hypotheses for simplifying the problem. In the last chapter of this first part, the maximum entropy on the mean method is presented: its origins are explained and also how it is applied to our problem. The second part is the original work of this thesis presenting three articles; one of them already published and two others submitted for publication. In the first article, a biomagnetic source model is developed and applied in a theoretical con text but still demonstrating the efficiency of the method. In the second article, we go one step further towards a realistic modelization of the cerebral activation. The main priors are estimated using the magnetoencephalographic data. This method proved to be very efficient in realistic simulations. In the third article, the previous method is extended to deal with time signals thus exploiting the excellent time resolution offered by magnetoencephalography. Compared with our previous work, the temporal method is applied to real magnetoencephalographic data coming from a somatotopy experience and results agree with previous physiological knowledge about this kind of cognitive process.
Subspace-based analysis of the ERT inverse problem
NASA Astrophysics Data System (ADS)
Ben Hadj Miled, Mohamed Khames; Miller, Eric L.
2004-05-01
In a previous work, we proposed a source-type formulation to the electrical resistance tomography (ERT) problem. Specifically, we showed that inhomogeneities in the medium can be viewed as secondary sources embedded in the homogeneous background medium and located at positions associated with variation in electrical conductivity. Assuming a piecewise constant conductivity distribution, the support of equivalent sources is equal to the boundary of the inhomogeneity. The estimation of the anomaly shape takes the form of an inverse source-type problem. In this paper, we explore the use of subspace methods to localize the secondary equivalent sources associated with discontinuities in the conductivity distribution. Our first alternative is the multiple signal classification (MUSIC) algorithm which is commonly used in the localization of multiple sources. The idea is to project a finite collection of plausible pole (or dipole) sources onto an estimated signal subspace and select those with largest correlations. In ERT, secondary sources are excited simultaneously but in different ways, i.e. with distinct amplitude patterns, depending on the locations and amplitudes of primary sources. If the number of receivers is "large enough", different source configurations can lead to a set of observation vectors that span the data subspace. However, since sources that are spatially close to each other have highly correlated signatures, seperation of such signals becomes very difficult in the presence of noise. To overcome this problem we consider iterative MUSIC algorithms like R-MUSIC and RAP-MUSIC. These recursive algorithms pose a computational burden as they require multiple large combinatorial searches. Results obtained with these algorithms using simulated data of different conductivity patterns are presented.
Haber, Eldad
2014-03-17
The focus of research was: Developing adaptive mesh for the solution of Maxwell's equations; Developing a parallel framework for time dependent inverse Maxwell's equations; Developing multilevel methods for optimization problems with inequal- ity constraints; A new inversion code for inverse Maxwell's equations in the 0th frequency (DC resistivity); A new inversion code for inverse Maxwell's equations in low frequency regime. Although the research concentrated on electromagnetic forward and in- verse problems the results of the research was applied to the problem of image registration.
NASA Astrophysics Data System (ADS)
Zhan, Qin; Yuan, Yuan; Fan, Xiangtao; Huang, Jianyong; Xiong, Chunyang; Yuan, Fan
2016-06-01
Digital image correlation (DIC) is essentially implicated in a class of inverse problem. Here, a regularization scheme is developed for the subset-based DIC technique to effectively inhibit potential ill-posedness that likely arises in actual deformation calculations and hence enhance numerical stability, accuracy and precision of correlation measurement. With the aid of a parameterized two-dimensional Butterworth window, a regularized subpixel registration strategy is established, in which the amount of speckle information introduced to correlation calculations may be weighted through equivalent subset size constraint. The optimal regularization parameter associated with each individual sampling point is determined in a self-adaptive way by numerically investigating the curve of 2-norm condition number of coefficient matrix versus the corresponding equivalent subset size, based on which the regularized solution can eventually be obtained. Numerical results deriving from both synthetic speckle images and actual experimental images demonstrate the feasibility and effectiveness of the set of newly-proposed regularized DIC algorithms.
NASA Astrophysics Data System (ADS)
Coleman, Thomas F.; Santosa, Fadil; Verma, Arun
2000-01-01
Wave propagational inverse problems arise in several applications including medical imaging and geophysical exploration. In these problems, one is interested in obtaining the parameters describing the medium from its response to excitations. The problems are characterized by their large size, and by the hyperbolic equation which models the physical phenomena. The inverse problems are often posed as a nonlinear data-fitting where the unknown parameters are found by minimizing the misfit between the predicted data and the actual data. In order to solve the problem numerically using a gradient-type approach, one must calculate the action of the Jacobian and its adjoint on a given vector. In this paper, we explore the use of automatic differentiation (AD) to develop codes that perform these calculations. We show that by exploiting structure at 2 scales, we can arrive at a very efficient code whose main components are produced by AD. In the first scale we exploite the time-stepping nature of the hyperbolic solver by using the “Extended Jacobian” framework. In the second (finer) scale, we exploit the finite difference stencil in order to make explicit use of the sparsity in the dependence of the output variables to the input variables. The main ideas in this work are illustrated with a simpler, one-dimensional version of the problem. Numerical results are given for both one- and two- dimensional problems. We present computational templates that can be used in conjunction with optimization packages to solve the inverse problem.
Toward the solution of the inverse problem in neutron reflectometry
Haan, V.O. de; Well, A.A. van; Sacks, P.E.; Adenwalla, S.; Felcher, G.P.
1995-08-01
The authors show that the chemical depth profile of a film of unknown structure can be retrieved unambiguously from neutron reflection data by adding to the system a known magnetic layer. Three independent reflectivities are obtained by taking measurements with the sample magnetized in a magnetic field perpendicular to the surface and subsequently parallel to it, and using in the latter geometry neutrons polarized either in the direction of the field or opposite to it. The procedure consists of two steps. First, from the three reflectivities both the real and imaginary parts of the reflection coefficient of the unknown film are extracted within the framework of the rigorous dynamical theory. Second, the neutron scattering-length density (and consequently the chemical depth profile) is obtained by a suitable numerical technique for the conventional Schroedinger inverse scattering problem. Computer experiments were conducted for selected cases: starting from the profiles the reflectivities were calculated in a limited range of q and then the original profiles were successfully recovered. The influence on the accuracy of the recovered depth profile of the counting statistics and the cutoffs at low and high q are discussed.
Solving Inverse Detection Problems Using Passive Radiation Signatures
Favorite, Jeffrey A.; Armstrong, Jerawan C.; Vaquer, Pablo A.
2012-08-15
The ability to reconstruct an unknown radioactive object based on its passive gamma-ray and neutron signatures is very important in homeland security applications. Often in the analysis of unknown radioactive objects, for simplicity or speed or because there is no other information, they are modeled as spherically symmetric regardless of their actual geometry. In these presentation we discuss the accuracy and implications of this approximation for decay gamma rays and for neutron-induced gamma rays. We discuss an extension of spherical raytracing (for uncollided fluxes) that allows it to be used when the exterior shielding is flat or cylindrical. We revisit some early results in boundary perturbation theory, showing that the Roussopolos estimate is the correct one to use when the quantity of interest is the flux or leakage on the boundary. We apply boundary perturbation theory to problems in which spherically symmetric systems are perturbed in asymmetric nonspherical ways. We apply mesh adaptive direct search (MADS) algorithms to object reconstructions. We present a benchmark test set that may be used to quantitatively evaluate inverse detection methods.
Inverse Problem for Harmonic Oscillator Perturbed by Potential, Characterization
NASA Astrophysics Data System (ADS)
Chelkak, Dmitri; Kargaev, Pavel; Korotyaev, Evgeni
Consider the perturbed harmonic oscillator Ty=-y''+x2y+q(x)y in L2(R), where the real potential q belongs to the Hilbert space H={q', xq∈ L2(R)}. The spectrum of T is an increasing sequence of simple eigenvalues λn(q)=1+2n+μn, n >= 0, such that μn--> 0 as n-->∞. Let ψn(x,q) be the corresponding eigenfunctions. Define the norming constants νn(q)=limx↑∞log |ψn (x,q)/ψn (-x,q)|. We show that for some real Hilbert space and some subspace Furthermore, the mapping ψ:q|-->ψ(q)=({λn(q)}0∞, {νn(q)}0∞) is a real analytic isomorphism between H and is the set of all strictly increasing sequences s={sn}0∞ such that The proof is based on nonlinear functional analysis combined with sharp asymptotics of spectral data in the high energy limit for complex potentials. We use ideas from the analysis of the inverse problem for the operator -y''py, p∈ L2(0,1), with Dirichlet boundary conditions on the unit interval. There is no literature about the spaces We obtain their basic properties, using their representation as spaces of analytic functions in the disk.
Inverse problem for the current loop model: Possibilities and restrictions
NASA Astrophysics Data System (ADS)
Demina, I. M.; Farafonova, Yu. G.
2016-07-01
The possibilities of determining arbitrary current loop parameters based on the spatial structures of the magnetic field components generated by this loop on a sphere with a specified radius have been considered with the use of models. The model parameters were selected such that anomalies created by current loops on a sphere with a radius of 6378 km would be comparable in value with the different-scale anomalies of the observed main geomagnetic field (MGF). The least squares method was used to solve the inverse problem. Estimates close to the specified values were obtained for all current loop parameters except the current strength and radius. The radius determination error can reach ±120 km; at the same time, the magnetic moment value is determined with an accuracy of ±1%. The resolvability of the current force and radius can to a certain degree be improved by decreasing the observation sphere radius such that the ratio of the source distance to the current loop radius would be at least smaller than eight, which can be difficult to reach when modeling MGF.
Sparse and redundant representations for inverse problems and recognition
NASA Astrophysics Data System (ADS)
Patel, Vishal M.
Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed
NASA Astrophysics Data System (ADS)
Arróyave, R.; Gibbons, S. L.; Galvan, E.; Malak, R. J.
2016-05-01
In general, the forward phase stability problem consists of mapping thermodynamic conditions (e.g., composition, temperature, pressure) to corresponding equilibrium states. In this paper, we instead focus on the generalized inverse phase stability problem (GIPSP) that deals with mapping a set of phase constitutions to a set of corresponding thermodynamic conditions. Specifically, we define the GIPSP as mapping of sets of phase constitution definitions in a multidimensional phase constitution search space to corresponding ranges of thermodynamic conditions. Mathematically, the solution to the GIPSP corresponds to all solutions to a continuous constraint satisfaction problem (CCSP). We present novel algorithms combining computational thermodynamics, evolutionary computation, and machine learning to approximate solution sets to the GIPSP as a CCSP. Some preliminary examples demonstrating the algorithms are presented. Moreover, the implications of the proposed framework for the larger problem of materials design are discussed, and future work is suggested.
Butler, T.; Graham, L.; Estep, D.; Westerink, J.J.
2015-01-01
The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed. PMID:25937695
Forward- vs. Inverse Problems in Modeling Seismic Attenuation
NASA Astrophysics Data System (ADS)
Morozov, I. B.
2015-12-01
Seismic attenuation is an important property of wave propagation used in numerous applications. However, the attenuation is also a complex phenomenon, and it is important to differentiate between its two typical uses: 1) in forward problems, to model the amplitudes and spectral contents of waves required for hazard assessment and geotechnical engineering, and 2) in inverse problems, to determine the physical properties of the subsurface. In the forward-problem sense, the attenuation is successfully characterized in terms of empirical parameters of geometric spreading, radiation patterns, scattering amplitudes, t-star, alpha, kappa, or Q. Arguably, the predicted energy losses can be correct even if the underlying attenuation model is phenomenological and not sufficiently based on physics. An example of such phenomenological model is the viscoelasticity based on the correspondence principle and the Q-factor assigned to the material. By contrast, when used to invert for in situ material properties, models addressing the specific physics are required. In many studies (including in this session), a Q-factor is interpreted as a property of a point within the subsurface; however this property is only phenomenological and may be physically insufficient or inconsistent. For example, the bulk or shear Q at the same point can be different when evaluated from different wave modes. The cases of frequency-dependent Q are particularly prone of ambiguities such as trade-off with the assumed background geometric spreading. To rigorously characterize the in situ material properties responsible for seismic-wave attenuation, it is insufficient to only focus on the seismic energy loss. Mechanical models of the material need to be considered. Such models can be constructed by using Lagrangian mechanics. These models should likely contain no Q but will be based on parameters of microstructure such as heterogeneity, fractures, or fluids. I illustrate several such models based on viscosity
Application of evolution strategies for the solution of an inverse problem in near-field optics.
Macías, Demetrio; Vial, Alexandre; Barchiesi, Dominique
2004-08-01
We introduce an inversion procedure for the characterization of a nanostructure from near-field intensity data. The method proposed is based on heuristic arguments and makes use of evolution strategies for the solution of the inverse problem as a nonlinear constrained-optimization problem. By means of some examples we illustrate the performance of our inversion method. We also discuss its possibilities and potential applications. PMID:15330475
Entire nodal solutions to the pure critical exponent problem arising from concentration
NASA Astrophysics Data System (ADS)
Clapp, Mónica
2016-09-01
We obtain new sign changing solutions to the problem We exhibit solutions up to (℘p) which blow up at a single point as p →2*, developing a peak whose asymptotic profile is a rescaling of a nonradial sign changing solution to problem (℘∞). We also obtain existence and multiplicity of sign changing nonradial solutions to the Bahri-Coron problem (℘2*) in annuli.
NASA Astrophysics Data System (ADS)
Oralsyn, Gulaym
2016-08-01
We study an inverse coefficient problem for a model equation for one-dimensional heat transfer with a preservation of medium temperature. It is needed (together with finding its solution) to find time dependent unknown coefficient of the equation. So, for this inverse problem, existence of an unique generalized solution is proved. The main difficulty of the considered problems is that the eigenfunction system of the corresponding boundary value problems does not have the basis property.
Model error estimation and correction by solving a inverse problem
NASA Astrophysics Data System (ADS)
Xue, Haile
2016-04-01
Nowadays, the weather forecasts and climate predictions are increasingly relied on numerical models. Yet, errors inevitably exist in model due to the imperfect numeric and parameterizations. From the practical point of view, model correction is an efficient strategy. Despite of the different complexity of forecast error correction algorithms, the general idea is to estimate the forecast errors by considering the NWP as a direct problem. Chou (1974) suggested an alternative view by considering the NWP as an inverse problem. The model error tendency term (ME) due to the model deficiency is assumed as an unknown term in NWP model, which can be discretized into short intervals (for example 6 hour) and considered as a constant or linear form in each interval. Given the past re-analyses and NWP model, the discretized MEs in the past intervals can be solved iteratively as a constant or linear-increased tendency term in each interval. These MEs can be further used as the online corrections. In this study, an iterative method for obtaining the MEs in past intervals was presented, and its convergence had been confirmed with sets of experiments in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August (JA) 2009 and January-February (JF) 2010. Then these MEs were used to get online model corretions based of systematic errors of GRAPES-GFS for July 2009 and January 2010. The data sets associated with initial condition and sea surface temperature (SST) used in this study are both based on NCEP final (FNL) data. According to the iterative numerical experiments, the following key conclusions can be drawn:(1) Batches of iteration test results indicated that the hour 6 forecast errors were reduced to 10% of their original value after 20 steps of iteration.(2) By offlinely comparing the error corrections estimated by MEs to the mean forecast errors, the patterns of estimated errors were considered to agree well with those
Restarted inverse Born series for the Schrödinger problem with discrete internal measurements
NASA Astrophysics Data System (ADS)
Bardsley, Patrick; Guevara Vasquez, Fernando
2014-04-01
Convergence and stability results for the inverse Born series (Moskow and Schotland 2008 Inverse Problems 24 065005) are generalized to mappings between Banach spaces. We show that by restarting the inverse Born series one obtains a class of iterative methods containing the Gauss-Newton and Chebyshev-Halley methods. We use the generalized inverse Born series results to show convergence of the inverse Born series for the Schrödinger problem with discrete internal measurements. In this problem, the Schrödinger potential is to be recovered from a few measurements of solutions to the Schrödinger equation resulting from a few different source terms. An application of this method to a problem related to transient hydraulic tomography is given, where the source terms model injection and measurement wells.
Khan, T.; Ramuhalli, Pradeep; Dass, Sarat
2011-06-30
Flaw profile characterization from NDE measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem, and subsequent application of a sequential Monte Carlo method called particle filtering, has been proposed by the authors in an earlier publication [1]. In this study, the problem of flaw characterization from multi-sensor data is considered. The NDE inverse problem is posed as a statistical inverse problem and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis (PCA) used to legitimize the assumption of independence. The proposed particle filter based data fusion algorithm is applied to experimental NDE data to investigate its feasibility.
ERIC Educational Resources Information Center
Brown, Malcolm
2009-01-01
Inversions are fascinating phenomena. They are reversals of the normal or expected order. They occur across a wide variety of contexts. What do inversions have to do with learning spaces? The author suggests that they are a useful metaphor for the process that is unfolding in higher education with respect to education. On the basis of…
Inverse problems in the design, modeling and testing of engineering systems
NASA Technical Reports Server (NTRS)
Alifanov, Oleg M.
1991-01-01
Formulations, classification, areas of application, and approaches to solving different inverse problems are considered for the design of structures, modeling, and experimental data processing. Problems in the practical implementation of theoretical-experimental methods based on solving inverse problems are analyzed in order to identify mathematical models of physical processes, aid in input data preparation for design parameter optimization, help in design parameter optimization itself, and to model experiments, large-scale tests, and real tests of engineering systems.
NASA Astrophysics Data System (ADS)
Ashyralyyev, Charyyar; Akyüz, Gulzipa
2016-08-01
In this study, we discuss well-posedness of Bitsadze-Samarskii type inverse elliptic problem with Dirichlet conditions. We establish abstract results on stability and coercive stability estimates for the solution of this inverse problem. Then, the abstract results are applied to three overdetermined problems for the multi-dimensional elliptic equation with different boundary conditions. Stability inequalities for solutions of these applications are obtained.
A boundary integral method for an inverse problem in thermal imaging
NASA Technical Reports Server (NTRS)
Bryan, Kurt
1992-01-01
An inverse problem in thermal imaging involving the recovery of a void in a material from its surface temperature response to external heating is examined. Uniqueness and continuous dependence results for the inverse problem are demonstrated, and a numerical method for its solution is developed. This method is based on an optimization approach, coupled with a boundary integral equation formulation of the forward heat conduction problem. Some convergence results for the method are proved, and several examples are presented using computationally generated data.
Reinforcement learning solution for HJB equation arising in constrained optimal control problem.
Luo, Biao; Wu, Huai-Ning; Huang, Tingwen; Liu, Derong
2015-11-01
The constrained optimal control problem depends on the solution of the complicated Hamilton-Jacobi-Bellman equation (HJBE). In this paper, a data-based off-policy reinforcement learning (RL) method is proposed, which learns the solution of the HJBE and the optimal control policy from real system data. One important feature of the off-policy RL is that its policy evaluation can be realized with data generated by other behavior policies, not necessarily the target policy, which solves the insufficient exploration problem. The convergence of the off-policy RL is proved by demonstrating its equivalence to the successive approximation approach. Its implementation procedure is based on the actor-critic neural networks structure, where the function approximation is conducted with linearly independent basis functions. Subsequently, the convergence of the implementation procedure with function approximation is also proved. Finally, its effectiveness is verified through computer simulations. PMID:26356598
Inversion problem for ion-atom differential elastic scattering.
NASA Technical Reports Server (NTRS)
Rich, W. G.; Bobbio, S. M.; Champion, R. L.; Doverspike, L. D.
1971-01-01
The paper describes a practical application of Remler's (1971) method by which one constructs a set of phase shifts from high resolution measurements of the differential elastic scattering of protons by rare-gas atoms. These JWKB phase shifts are then formally inverted to determine the corresponding intermolecular potentials. The validity of the method is demonstrated by comparing an intermolecular potential obtained by direct inversion of experimental data with a fairly accurate calculation by Wolniewicz (1965).
The incomplete inverse and its applications to the linear least squares problem
NASA Technical Reports Server (NTRS)
Morduch, G. E.
1977-01-01
A modified matrix product is explained, and it is shown that this product defiles a group whose inverse is called the incomplete inverse. It was proven that the incomplete inverse of an augmented normal matrix includes all the quantities associated with the least squares solution. An answer is provided to the problem that occurs when the data residuals are too large and when insufficient data to justify augmenting the model are available.
NASA Astrophysics Data System (ADS)
Mont, Alexander D.; Calderon, Christopher P.; Poore, Aubrey B.
2014-06-01
We present a new approach to estimating the probability of each association in a 2D assignment problem defined by likelihood ratios. Our method divides the set of feasible hypotheses into clusters, and converts a collection of hypotheses into a collection of clusters containing them, reducing the variance of the estimate. Simulations show that our method often generates substantially more accurate probability estimates in less time than traditional methods. Our method can obtain reasonably accurate probabilities of association based on only the input cost matrix and single best candidate solution, eliminating the need for a K-best solution method or MCMC sampling.
NASA Astrophysics Data System (ADS)
Bürger, Raimund; Kumar, Sarvesh; Ruiz-Baier, Ricardo
2015-10-01
The sedimentation-consolidation and flow processes of a mixture of small particles dispersed in a viscous fluid at low Reynolds numbers can be described by a nonlinear transport equation for the solids concentration coupled with the Stokes problem written in terms of the mixture flow velocity and the pressure field. Here both the viscosity and the forcing term depend on the local solids concentration. A semi-discrete discontinuous finite volume element (DFVE) scheme is proposed for this model. The numerical method is constructed on a baseline finite element family of linear discontinuous elements for the approximation of velocity components and concentration field, whereas the pressure is approximated by piecewise constant elements. The unique solvability of both the nonlinear continuous problem and the semi-discrete DFVE scheme is discussed, and optimal convergence estimates in several spatial norms are derived. Properties of the model and the predicted space accuracy of the proposed formulation are illustrated by detailed numerical examples, including flows under gravity with changing direction, a secondary settling tank in an axisymmetric setting, and batch sedimentation in a tilted cylindrical vessel.
NASA Astrophysics Data System (ADS)
Jiang, Mingfeng; Xia, Ling; Shou, Guofa; Tang, Min
2007-03-01
Computing epicardial potentials from body surface potentials constitutes one form of ill-posed inverse problem of electrocardiography (ECG). To solve this ECG inverse problem, the Tikhonov regularization and truncated singular-value decomposition (TSVD) methods have been commonly used to overcome the ill-posed property by imposing constraints on the magnitudes or derivatives of the computed epicardial potentials. Such direct regularization methods, however, are impractical when the transfer matrix is large. The least-squares QR (LSQR) method, one of the iterative regularization methods based on Lanczos bidiagonalization and QR factorization, has been shown to be numerically more reliable in various circumstances than the other methods considered. This LSQR method, however, to our knowledge, has not been introduced and investigated for the ECG inverse problem. In this paper, the regularization properties of the Krylov subspace iterative method of LSQR for solving the ECG inverse problem were investigated. Due to the 'semi-convergence' property of the LSQR method, the L-curve method was used to determine the stopping iteration number. The performance of the LSQR method for solving the ECG inverse problem was also evaluated based on a realistic heart-torso model simulation protocol. The results show that the inverse solutions recovered by the LSQR method were more accurate than those recovered by the Tikhonov and TSVD methods. In addition, by combing the LSQR with genetic algorithms (GA), the performance can be improved further. It suggests that their combination may provide a good scheme for solving the ECG inverse problem.
NASA Astrophysics Data System (ADS)
Corrado, Cesare; Gerbeau, Jean-Frédéric; Moireau, Philippe
2015-02-01
This work addresses the inverse problem of electrocardiography from a new perspective, by combining electrical and mechanical measurements. Our strategy relies on the definition of a model of the electromechanical contraction which is registered on ECG data but also on measured mechanical displacements of the heart tissue typically extracted from medical images. In this respect, we establish in this work the convergence of a sequential estimator which combines for such coupled problems various state of the art sequential data assimilation methods in a unified consistent and efficient framework. Indeed, we aggregate a Luenberger observer for the mechanical state and a Reduced-Order Unscented Kalman Filter applied on the parameters to be identified and a POD projection of the electrical state. Then using synthetic data we show the benefits of our approach for the estimation of the electrical state of the ventricles along the heart beat compared with more classical strategies which only consider an electrophysiological model with ECG measurements. Our numerical results actually show that the mechanical measurements improve the identifiability of the electrical problem allowing to reconstruct the electrical state of the coupled system more precisely. Therefore, this work is intended to be a first proof of concept, with theoretical justifications and numerical investigations, of the advantage of using available multi-modal observations for the estimation and identification of an electromechanical model of the heart.
New problems arising from old drugs: second-generation effects of acetaminophen.
Tiegs, Gisa; Karimi, Khalil; Brune, Kay; Arck, Petra
2014-09-01
Acetaminophen (APAP)/paracetamol is one of the most commonly used over-the-counter drugs taken worldwide for treatment of pain and fever. Although considered as safe when taken in recommended doses not higher than 4 g/day, APAP overdose is currently the most important cause of acute liver failure (ALF). ALF may require liver transplantation and can be fatal. The reasons for APAP-related ALF are mostly intentional (suicidal) or unintentional overdose. However, results from large scale epidemiological studies provide increasing evidence for second generation effects of APAP, even when taken in pharmacological doses. Most strikingly, APAP medication during pregnancy has been associated with health problems including neurodevelopmental and behavioral disorders such as attention deficit hyperactivity disorder and increase in the risk of wheezing and incidence of asthma among offspring. This article reviews the epidemiological findings and aims to shed light into the molecular and cellular mechanisms responsible for APAP-mediated prenatal risk for asthma. PMID:25075430
Application of spectral Lanczos decomposition method to large scale problems arising geophysics
Tamarchenko, T.
1996-12-31
This paper presents an application of Spectral Lanczos Decomposition Method (SLDM) to numerical modeling of electromagnetic diffusion and elastic waves propagation in inhomogeneous media. SLDM approximates an action of a matrix function as a linear combination of basis vectors in Krylov subspace. I applied the method to model electromagnetic fields in three-dimensions and elastic waves in two dimensions. The finite-difference approximation of the spatial part of differential operator reduces the initial boundary-value problem to a system of ordinary differential equations with respect to time. The solution to this system requires calculating exponential and sine/cosine functions of the stiffness matrices. Large scale numerical examples are in a good agreement with the theoretical error bounds and stability estimates given by Druskin, Knizhnerman, 1987.
Remark on boundary data for inverse boundary value problems for the Navier-Stokes equations
NASA Astrophysics Data System (ADS)
Imanuvilov, O. Yu; Yamamoto, M.
2015-10-01
In this note, we prove that for the Navier-Stokes equations, a pair of Dirichlet and Neumann data and pressure uniquely correspond to a pair of Dirichlet data and surface stress on the boundary. Hence the two inverse boundary value problems in Imanuvilov and Yamamoto (2015 Inverse Probl. 31 035004) and Lai et al (Arch. Rational Mech. Anal.) are the same.
NASA Astrophysics Data System (ADS)
Senses, Begum
A state-defect constraint pairing graph coarsening method is described for improving computational efficiency during the numerical factorization of large sparse Karush-Kuhn-Tucker matrices that arise from the discretization of optimal control problems via a Legendre-Gauss-Radau orthogonal collocation method. The method takes advantage of the particular sparse structure of the Karush-Kuhn-Tucker matrix that arises from the orthogonal collocation method. The state-defect constraint pairing graph coarsening method pairs each component of the state with its corresponding defect constraint and forces paired rows to be adjacent in the reordered Karush-Kuhn-Tucker matrix. Aggregate state-defect constraint pairing results are presented using a wide variety of benchmark optimal control problems where it is found that the proposed state-defect constraint pairing graph coarsening method significantly reduces both the number of delayed pivots and the number of floating point operations and increases the computational efficiency by performing more floating point operations per unit time. It is then shown that the state-defect constraint pairing graph coarsening method is less effective on Karush-Kuhn-Tucker matrices arising from Legendre-Gauss-Radau collocation when the optimal control problem contains state and control equality path constraints because such matrices may have delayed pivots that correspond to both defect and path constraints. An unweighted alternate graph coarsening method that employs maximal matching and a weighted alternate graph coarsening method that employs Hungarian algorithm on a weighting matrix are then used to attempt to further reduce the number of delayed pivots. It is found, however, that these alternate graph coarsening methods provide no further advantage over the state-defect constraint pairing graph coarsening method.
A parameter identification problem arising from a two-dimensional airfoil section model
Cerezo, G.M.
1994-12-31
The development of state space models for aeroelastic systems, including unsteady aerodynamics, is particularly important for the design of highly maneuverable aircraft. In this work we present a state space formulation for a special class of singular neutral functional differential equations (SNFDE) with initial data in C(-1, 0). This work is motivated by the two-dimensional airfoil model presented by Burns, Cliff and Herdman in. In the same authors discuss the validity of the assumptions under which the model was formulated. They pay special attention to the derivation of the evolution equation for the circulation on the airfoil. This equation was coupled to the rigid-body dynamics of the airfoil in order to obtain a complete set of functional differential equations that describes the composite system. The resulting mathematical model for the aeroelastic system has a weakly singular component. In this work we consider a finite delay approximation to the model presented in. We work with a scalar model in which we consider the weak singularity appearing in the original problem. The main goal of this work is to develop numerical techniques for the identification of the parameters appearing in the kernel of the associated scalar integral equation. Clearly this is the first step in the study of parameter identification for the original model and the corresponding validation of this model for the aeroelastic system.
NASA Astrophysics Data System (ADS)
Cheng, Jin; Hon, Yiu-Chung; Seo, Jin Keun; Yamamoto, Masahiro
2005-01-01
The Second International Conference on Inverse Problems: Recent Theoretical Developments and Numerical Approaches was held at Fudan University, Shanghai from 16-21 June 2004. The first conference in this series was held at the City University of Hong Kong in January 2002 and it was agreed to hold the conference once every two years in a Pan-Pacific Asian country. The next conference is scheduled to be held at Hokkaido University, Sapporo, Japan in July 2006. The purpose of this series of biennial conferences is to establish and develop constant international collaboration, especially among the Pan-Pacific Asian countries. In recent decades, interest in inverse problems has been flourishing all over the globe because of both the theoretical interest and practical requirements. In particular, in Asian countries, one is witnessing remarkable new trends of research in inverse problems as well as the participation of many young talents. Considering these trends, the second conference was organized with the chairperson Professor Li Tat-tsien (Fudan University), in order to provide forums for developing research cooperation and to promote activities in the field of inverse problems. Because solutions to inverse problems are needed in various applied fields, we entertained a total of 92 participants at the second conference and arranged various talks which ranged from mathematical analyses to solutions of concrete inverse problems in the real world. This volume contains 18 selected papers, all of which have undergone peer review. The 18 papers are classified as follows: Surveys: four papers give reviews of specific inverse problems. Theoretical aspects: six papers investigate the uniqueness, stability, and reconstruction schemes. Numerical methods: four papers devise new numerical methods and their applications to inverse problems. Solutions to applied inverse problems: four papers discuss concrete inverse problems such as scattering problems and inverse problems in
Comparing hard and soft prior bounds in geophysical inverse problems
NASA Technical Reports Server (NTRS)
Backus, George E.
1988-01-01
In linear inversion of a finite-dimensional data vector y to estimate a finite-dimensional prediction vector z, prior information about X sub E is essential if y is to supply useful limits for z. The one exception occurs when all the prediction functionals are linear combinations of the data functionals. Two forms of prior information are compared: a soft bound on X sub E is a probability distribution p sub x on X which describes the observer's opinion about where X sub E is likely to be in X; a hard bound on X sub E is an inequality Q sub x(X sub E, X sub E) is equal to or less than 1, where Q sub x is a positive definite quadratic form on X. A hard bound Q sub x can be softened to many different probability distributions p sub x, but all these p sub x's carry much new information about X sub E which is absent from Q sub x, and some information which contradicts Q sub x. Both stochastic inversion (SI) and Bayesian inference (BI) estimate z from y and a soft prior bound p sub x. If that probability distribution was obtained by softening a hard prior bound Q sub x, rather than by objective statistical inference independent of y, then p sub x contains so much unsupported new information absent from Q sub x that conclusions about z obtained with SI or BI would seen to be suspect.
Comparing hard and soft prior bounds in geophysical inverse problems
NASA Technical Reports Server (NTRS)
Backus, George E.
1987-01-01
In linear inversion of a finite-dimensional data vector y to estimate a finite-dimensional prediction vector z, prior information about X sub E is essential if y is to supply useful limits for z. The one exception occurs when all the prediction functionals are linear combinations of the data functionals. Two forms of prior information are compared: a soft bound on X sub E is a probability distribution p sub x on X which describeds the observer's opinion about where X sub E is likely to be in X; a hard bound on X sub E is an inequality Q sub x(X sub E, X sub E) is equal to or less than 1, where Q sub x is a positive definite quadratic form on X. A hard bound Q sub x can be softened to many different probability distributions p sub x, but all these p sub x's carry much new information about X sub E which is absent from Q sub x, and some information which contradicts Q sub x. Both stochastic inversion (SI) and Bayesian inference (BI) estimate z from y and a soft prior bound p sub x. If that probability distribution was obtained by softening a hard prior bound Q sub x, rather than by objective statistical inference independent of y, then p sub x contains so much unsupported new information absent from Q sub x that conclusions about z obtained with SI or BI would seen to be suspect.
Global solution to a hyperbolic problem arising in the modeling of blood flow in circulatory systems
NASA Astrophysics Data System (ADS)
Ruan, Weihua; Clark, M. E.; Zhao, Meide; Curcio, Anthony
2007-07-01
This paper considers a system of first-order, hyperbolic, partial differential equations in the domain of a one-dimensional network. The system models the blood flow in human circulatory systems as an initial-boundary-value problem with boundary conditions of either algebraic or differential type. The differential equations are nonhomogeneous with frictional damping terms and the state variables are coupled at internal junctions. The existence and uniqueness of the local classical solution have been established in our earlier work [W. Ruan, M.E. Clark, M. Zhao, A. Curcio, A hyperbolic system of equations of blood flow in an arterial network, J. Appl. Math. 64 (2) (2003) 637-667; W. Ruan, M.E. Clark, M. Zhao, A. Curcio, Blood flow in a network, Nonlinear Anal. Real World Appl. 5 (2004) 463-485; W. Ruan, M.E. Clark, M. Zhao, A. Curcio, A quasilinear hyperbolic system that models blood flow in a network, in: Charles V. Benton (Ed.), Focus on Mathematical Physics Research, Nova Science Publishers, Inc., New York, 2004, pp. 203-230]. This paper continues the analysis and gives sufficient conditions for the global existence of the classical solution. We prove that the solution exists globally if the boundary data satisfy the dissipative condition (2.3) or (3.2), and the norms of the initial and forcing functions in a certain Sobolev space are sufficiently small. This is only the first step toward establishing the global existence of the solution to physiologically realistic models, because, in general, the chosen dissipative conditions (2.3) and (3.2) do not appear to hold for the originally proposed boundary conditions (1.3)-(1.12).
NASA Astrophysics Data System (ADS)
Grigoriev, M.; Babich, L.
2015-09-01
The article represents the main noninvasive methods of heart electrical activity examination, theoretical bases of solution of electrocardiography inverse problem, application of different methods of heart examination in clinical practice, and generalized achievements in this sphere in global experience.
NASA Astrophysics Data System (ADS)
Denisov, A. M.; Zakharov, E. V.; Kalinin, A. V.; Kalinin, V. V.
2010-07-01
A numerical method is proposed for solving an inverse electrocardiography problem for a medium with a piecewise constant electrical conductivity. The method is based on the method of boundary integral equations and Tikhonov regularization.
Average synaptic activity and neural networks topology: a global inverse problem
NASA Astrophysics Data System (ADS)
Burioni, Raffaella; Casartelli, Mario; di Volo, Matteo; Livi, Roberto; Vezzani, Alessandro
2014-03-01
The dynamics of neural networks is often characterized by collective behavior and quasi-synchronous events, where a large fraction of neurons fire in short time intervals, separated by uncorrelated firing activity. These global temporal signals are crucial for brain functioning. They strongly depend on the topology of the network and on the fluctuations of the connectivity. We propose a heterogeneous mean-field approach to neural dynamics on random networks, that explicitly preserves the disorder in the topology at growing network sizes, and leads to a set of self-consistent equations. Within this approach, we provide an effective description of microscopic and large scale temporal signals in a leaky integrate-and-fire model with short term plasticity, where quasi-synchronous events arise. Our equations provide a clear analytical picture of the dynamics, evidencing the contributions of both periodic (locked) and aperiodic (unlocked) neurons to the measurable average signal. In particular, we formulate and solve a global inverse problem of reconstructing the in-degree distribution from the knowledge of the average activity field. Our method is very general and applies to a large class of dynamical models on dense random networks.
Review of the inverse scattering problem at fixed energy in quantum mechanics
NASA Technical Reports Server (NTRS)
Sabatier, P. C.
1972-01-01
Methods of solution of the inverse scattering problem at fixed energy in quantum mechanics are presented. Scattering experiments of a beam of particles at a nonrelativisitic energy by a target made up of particles are analyzed. The Schroedinger equation is used to develop the quantum mechanical description of the system and one of several functions depending on the relative distance of the particles. The inverse problem is the construction of the potentials from experimental measurements.
Some Inverse Problems in Periodic Homogenization of Hamilton-Jacobi Equations
NASA Astrophysics Data System (ADS)
Luo, Songting; Tran, Hung V.; Yu, Yifeng
2016-03-01
We look at the effective Hamiltonian {overline{H}} associated with the Hamiltonian {H(p,x)=H(p)+V(x)} in the periodic homogenization theory. Our central goal is to understand the relation between {V} and {overline{H}} . We formulate some inverse problems concerning this relation. Such types of inverse problems are, in general, very challenging. In this paper, we discuss several special cases in both convex and nonconvex settings.
Some Inverse Problems in Periodic Homogenization of Hamilton-Jacobi Equations
NASA Astrophysics Data System (ADS)
Luo, Songting; Tran, Hung V.; Yu, Yifeng
2016-09-01
We look at the effective Hamiltonian {overline{H}} associated with the Hamiltonian {H(p,x)=H(p)+V(x)} in the periodic homogenization theory. Our central goal is to understand the relation between {V} and {overline{H}}. We formulate some inverse problems concerning this relation. Such types of inverse problems are, in general, very challenging. In this paper, we discuss several special cases in both convex and nonconvex settings.
Sedimentary Facies Analysis Using AVIRIS Data: A Geophysical Inverse Problem
NASA Technical Reports Server (NTRS)
Boardmann, Joe W.; Goetz, Alexander F. H.
1990-01-01
AVIRIS data can be used to quantitatively analyze and map sedimentary lithofacies. The observed radiance spectra can be reduced to 'apparent reflectance' spectra by topographic and reflectance characterization of several field sites within the image. These apparent reflectance spectra correspond to the true reflectance at each pixel, multiplied by an unknown illumination factor (ranging in value from zero to one). The spatial abundance patterns of spectrally defined lithofacies and the unknown illumination factors can be simultaneously derived using constrained linear spectral unmixing methods. Estimates of the minimum uncertainty in the final results (due to noise, instrument resolutions, degree of illumination and mixing systematics) can be made by forward and inverse modeling. Specific facies studies in the Rattlesnake Hills region of Wyoming illustrate the successful application of these methods.
On solvability of inverse coefficient problems for nonlinear convection—diffusion—reaction equation
NASA Astrophysics Data System (ADS)
Brizitskii, R. V.; Saritskaya, Zh Yu
2016-06-01
An inverse coefficient problem is considered for a stationary nonlinear convection- diffusion-reaction equation, in which reaction coefficient has a rather common dependence on substance concentration and on spacial variable. The solvability of the considered nonlinear boundary value problem is proved in a general case. The existence of solutions of the inverse problem is proved for the reaction coefficients, which are defined by the product of two functions. The first function depends on a spatial variable, the second one depends nonlinearly on the solution of the boundary value problem. The mentioned inverse problem consists in reconstructing the first function with the help of additional information provided by the solution of the boundary value problem.
FOREWORD: 5th International Workshop on New Computational Methods for Inverse Problems
NASA Astrophysics Data System (ADS)
Vourc'h, Eric; Rodet, Thomas
2015-11-01
This volume of Journal of Physics: Conference Series is dedicated to the scientific research presented during the 5th International Workshop on New Computational Methods for Inverse Problems, NCMIP 2015 (http://complement.farman.ens-cachan.fr/NCMIP_2015.html). This workshop took place at Ecole Normale Supérieure de Cachan, on May 29, 2015. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of ValueTools Conference, in May 2011, and secondly at the initiative of Institut Farman, in May 2012, May 2013 and May 2014. The New Computational Methods for Inverse Problems (NCMIP) workshop focused on recent advances in the resolution of inverse problems. Indeed, inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, Kernel methods, learning methods
FOREWORD: 4th International Workshop on New Computational Methods for Inverse Problems (NCMIP2014)
NASA Astrophysics Data System (ADS)
2014-10-01
This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 4th International Workshop on New Computational Methods for Inverse Problems, NCMIP 2014 (http://www.farman.ens-cachan.fr/NCMIP_2014.html). This workshop took place at Ecole Normale Supérieure de Cachan, on May 23, 2014. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/), and secondly at the initiative of Institut Farman, in May 2012 and May 2013, (http://www.farman.ens-cachan.fr/NCMIP_2012.html), (http://www.farman.ens-cachan.fr/NCMIP_2013.html). The New Computational Methods for Inverse Problems (NCMIP) Workshop focused on recent advances in the resolution of inverse problems. Indeed, inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the
Ivanov, J.; Miller, R.D.; Xia, J.; Steeples, D.; Park, C.B.
2005-01-01
In a set of two papers we study the inverse problem of refraction travel times. The purpose of this work is to use the study as a basis for development of more sophisticated methods for finding more reliable solutions to the inverse problem of refraction travel times, which is known to be nonunique. The first paper, "Types of Geophysical Nonuniqueness through Minimization," emphasizes the existence of different forms of nonuniqueness in the realm of inverse geophysical problems. Each type of nonuniqueness requires a different type and amount of a priori information to acquire a reliable solution. Based on such coupling, a nonuniqueness classification is designed. Therefore, since most inverse geophysical problems are nonunique, each inverse problem must be studied to define what type of nonuniqueness it belongs to and thus determine what type of a priori information is necessary to find a realistic solution. The second paper, "Quantifying Refraction Nonuniqueness Using a Three-layer Model," serves as an example of such an approach. However, its main purpose is to provide a better understanding of the inverse refraction problem by studying the type of nonuniqueness it possesses. An approach for obtaining a realistic solution to the inverse refraction problem is planned to be offered in a third paper that is in preparation. The main goal of this paper is to redefine the existing generalized notion of nonuniqueness and a priori information by offering a classified, discriminate structure. Nonuniqueness is often encountered when trying to solve inverse problems. However, possible nonuniqueness diversity is typically neglected and nonuniqueness is regarded as a whole, as an unpleasant "black box" and is approached in the same manner by applying smoothing constraints, damping constraints with respect to the solution increment and, rarely, damping constraints with respect to some sparse reference information about the true parameters. In practice, when solving geophysical
NASA Astrophysics Data System (ADS)
Tenorio, L.; Haber, E.; Symes, W. W.; Stark, P. B.; Cox, D.; Ghattas, O.
2008-06-01
In the words of D D Jackson, the data of real-world inverse problems tend to be inaccurate, insufficient and inconsistent (1972 Geophys. J. R. Astron. Soc. 28 97-110). In view of these features, the characterization of solution uncertainty is an essential aspect of the study of inverse problems. The development of computational technology, in particular of multiscale and adaptive methods and robust optimization algorithms, has combined with advances in statistical methods in recent years to create unprecedented opportunities to understand and explore the role of uncertainty in inversion. Following this introductory article, the special section contains 16 papers describing recent statistical and computational advances in a variety of inverse problem settings.
Using a derivative-free optimization method for multiple solutions of inverse transport problems
Armstrong, Jerawan C.; Favorite, Jeffrey A.
2016-01-14
Identifying unknown components of an object that emits radiation is an important problem for national and global security. Radiation signatures measured from an object of interest can be used to infer object parameter values that are not known. This problem is called an inverse transport problem. An inverse transport problem may have multiple solutions and the most widely used approach for its solution is an iterative optimization method. This paper proposes a stochastic derivative-free global optimization algorithm to find multiple solutions of inverse transport problems. The algorithm is an extension of a multilevel single linkage (MLSL) method where a meshmore » adaptive direct search (MADS) algorithm is incorporated into the local phase. Furthermore, numerical test cases using uncollided fluxes of discrete gamma-ray lines are presented to show the performance of this new algorithm.« less
Hemmelmayr, Vera C.; Cordeau, Jean-François; Crainic, Teodor Gabriel
2012-01-01
In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP. PMID:23483764
Extremal norms of the potentials recovered from inverse Dirichlet problems
NASA Astrophysics Data System (ADS)
Qi, Jiangang; Chen, Shaozhu
2016-03-01
Consider the Sturm-Liouville eigenvalue problem -y\\prime\\prime (x)+q(x)y(x)=λ y(x),x\\in [0,1],y(0)=y(1)=0, where q\\in {L}1[0,1], and its spectrum is denoted by σ (q). For a real number λ, define {{Ω }}(λ )=\\{q\\in {L}1[0,1] :λ \\in σ (q)\\} and E(λ )={inf}\\{\\parallel q\\parallel :q\\in {{Ω }}(λ )\\}. We will set up a formula for E(λ ) explicitly in terms of λ and specify where the infimum can be attained. As an application, we will give the extremal values of the nth eigenvalue of the Dirichlet problem for potentials on a sphere {L}1[0,1], n≥slant 1. The proofs are based on a new Lyapunov-type inequality for Sturm-Liouville equations with potentials.
Statistical method for resolving the photon-photoelectron-counting inversion problem
Wu Jinlong; Li Tiejun; Peng, Xiang; Guo Hong
2011-02-01
A statistical inversion method is proposed for the photon-photoelectron-counting statistics in quantum key distribution experiment. With the statistical viewpoint, this problem is equivalent to the parameter estimation for an infinite binomial mixture model. The coarse-graining idea and Bayesian methods are applied to deal with this ill-posed problem, which is a good simple example to show the successful application of the statistical methods to the inverse problem. Numerical results show the applicability of the proposed strategy. The coarse-graining idea for the infinite mixture models should be general to be used in the future.
Unrealistic parameter estimates in inverse modelling: A problem or a benefit for model calibration?
Poeter, E.P.; Hill, M.C.
1996-01-01
Estimation of unrealistic parameter values by inverse modelling is useful for constructed model discrimination. This utility is demonstrated using the three-dimensional, groundwater flow inverse model MODFLOWP to estimate parameters in a simple synthetic model where the true conditions and character of the errors are completely known. When a poorly constructed model is used, unreasonable parameter values are obtained even when using error free observations and true initial parameter values. This apparent problem is actually a benefit because it differentiates accurately and inaccurately constructed models. The problems seem obvious for a synthetic problem in which the truth is known, but are obscure when working with field data. Situations in which unrealistic parameter estimates indicate constructed model problems are illustrated in applications of inverse modelling to three field sites and to complex synthetic test cases in which it is shown that prediction accuracy also suffers when constructed models are inaccurate.
NASA Technical Reports Server (NTRS)
Seidman, T. I.; Munteanu, M. J.
1979-01-01
The relationships of a variety of general computational methods (and variances) for treating illposed problems such as geophysical inverse problems are considered. Differences in approach and interpretation based on varying assumptions as to, e.g., the nature of measurement uncertainties are discussed along with the factors to be considered in selecting an approach. The reliability of the results of such computation is addressed.
Moment inversion problem for piecewise D-finite functions
NASA Astrophysics Data System (ADS)
Batenkov, Dmitry
2009-10-01
We consider the problem of exact reconstruction of univariate functions with jump discontinuities at unknown positions from their moments. These functions are assumed to satisfy an a priori unknown linear homogeneous differential equation with polynomial coefficients on each continuity interval. Therefore, they may be specified by a finite amount of information. This reconstruction problem has practical importance in signal processing and other applications. It is somewhat of a 'folklore' that the sequence of the moments of such 'piecewise D-finite' functions satisfies a linear recurrence relation of bounded order and degree. We derive this recurrence relation explicitly. It turns out that the coefficients of the differential operator which annihilates every piece of the function, as well as the locations of the discontinuities, appear in this recurrence in a precisely controlled manner. This leads to the formulation of a generic algorithm for reconstructing a piecewise D-finite function from its moments. We investigate the conditions for solvability of resulting linear systems in the general case, as well as analyse a few particular examples. We provide results of numerical simulations for several types of signals, which test the sensitivity of the proposed algorithm to noise.
Some speed-up strategies for solving inverse radiative transfer problems
NASA Astrophysics Data System (ADS)
Favennec, Y.; Le Hardy, D.; Dubot, F.; Rousseau, B.; Rousse, D. R.
2016-01-01
Inversion based on the radiative transfer equation (RTE) is generally highly CPU time consuming because the forward model itself is complicated to solve when the space dimension is greater than one, and because the inversion is based on a large number of forward model runs until convergence is reached. The goal of this paper is to set up some speed-up strategies specific of inversion when radiative transfer problems are dealt with. More specifically, the accurate identification of the volumetric radiative properties i.e. both the absorption and scattering coefficients is the objective of this study.
From Bayes to Tarantola: New insights to understand uncertainty in inverse problems
NASA Astrophysics Data System (ADS)
Fernández-Martínez, J. L.; Fernández-Muñiz, Z.; Pallero, J. L. G.; Pedruelo-González, L. M.
2013-11-01
Anyone working on inverse problems is aware of their ill-posed character. In the case of inverse problems, this concept (ill-posed) proposed by J. Hadamard in 1902, admits revision since it is somehow related to their ill-conditioning and the use of local optimization methods to find their solution. A more general and interesting approach regarding risk analysis and epistemological decision making would consist in analyzing the existence of families of equivalent model parameters that are compatible with the prior information and predict the observed data within the same error bounds. Otherwise said, the ill-posed character of discrete inverse problems (ill-conditioning) originates that their solution is uncertain. Traditionally nonlinear inverse problems in discrete form have been solved via local optimization methods with regularization, but linear analysis techniques failed to account for the uncertainty in the solution that it is adopted. As a result of this fact uncertainty analysis in nonlinear inverse problems has been approached in a probabilistic framework (Bayesian approach), but these methods are hindered by the curse of dimensionality and by the high computational cost needed to solve the corresponding forward problems. Global optimization techniques are very attractive, but most of the times are heuristic and have the same limitations than Monte Carlo methods. New research is needed to provide uncertainty estimates, especially in the case of high dimensional nonlinear inverse problems with very costly forward problems. After the discredit of deterministic methods and some initial years of Bayesian fever, now the pendulum seems to return back, because practitioners are aware that the uncertainty analysis in high dimensional nonlinear inverse problems cannot (and should not be) solved via random sampling methodologies. The main reason is that the uncertainty “space” of nonlinear inverse problems has a mathematical structure that is embedded in the
Fast and accurate analytical model to solve inverse problem in SHM using Lamb wave propagation
NASA Astrophysics Data System (ADS)
Poddar, Banibrata; Giurgiutiu, Victor
2016-04-01
Lamb wave propagation is at the center of attention of researchers for structural health monitoring of thin walled structures. This is due to the fact that Lamb wave modes are natural modes of wave propagation in these structures with long travel distances and without much attenuation. This brings the prospect of monitoring large structure with few sensors/actuators. However the problem of damage detection and identification is an "inverse problem" where we do not have the luxury to know the exact mathematical model of the system. On top of that the problem is more challenging due to the confounding factors of statistical variation of the material and geometric properties. Typically this problem may also be ill posed. Due to all these complexities the direct solution of the problem of damage detection and identification in SHM is impossible. Therefore an indirect method using the solution of the "forward problem" is popular for solving the "inverse problem". This requires a fast forward problem solver. Due to the complexities involved with the forward problem of scattering of Lamb waves from damages researchers rely primarily on numerical techniques such as FEM, BEM, etc. But these methods are slow and practically impossible to be used in structural health monitoring. We have developed a fast and accurate analytical forward problem solver for this purpose. This solver, CMEP (complex modes expansion and vector projection), can simulate scattering of Lamb waves from all types of damages in thin walled structures fast and accurately to assist the inverse problem solver.
Observation and inverse problems in coupled cell networks
NASA Astrophysics Data System (ADS)
Joly, Romain
2012-03-01
A coupled cell network is a model for many situations such as food webs in ecosystems, cellular metabolism and economic networks. It consists in a directed graph G, each node (or cell) representing an agent of the network and each directed arrow representing which agent acts on which. It yields a system of differential equations \\dot x(t)=f(x(t)) , where the component i of f depends only on the cells xj(t) for which the arrow j → i exists in G. In this paper, we investigate the observation problems in coupled cell networks: can one deduce the behaviour of the whole network (oscillations, stabilization, etc) by observing only one of the cells? We show that the natural observation properties hold for almost all the interactions f.
Zatsiorsky, Vladimir M.
2011-01-01
One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423–453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem. PMID:21311907
Terekhov, Alexander V; Zatsiorsky, Vladimir M
2011-02-01
One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423-453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem. PMID:21311907
Reification of galaxies: cognitive astrophysics and the multiwavelength inverse problem
NASA Astrophysics Data System (ADS)
Madore, Barry F.
2012-08-01
Lessons learned in the history and philosophy of science have generally had little immediate impact on how we as individual astronomers conduct our research. And yet we do share many common views on how we undertake basic research, and how we translate observations and theory into communicable knowledge. In this introductory talk I will illustrate how we as extragalactic astronomers have already violated some of the basic tenets of what constitutes ``science'' as seen from a philosophical point of view, and I will predict what the future of astronomy as a science may soon look like. Simple examples of how we are already ``cognitively closed'' to many immediate and tangible aspects of the Universe will be given and some solutions to this dilemma will be proposed. We may be at a point in time where more data is not necessarily the best solution to our problems. Discovering that familiar concepts and even certain objects may not exist in the traditional sense of the word could provide a motivation for broadening our way of conceptualizing the extragalactic Universe, more as a continuum of processes and phase transitions rather than an assembly of discrete objects. Once again the Universe may be ``forcing us to think''.
Removal of numerical instability in the solution of an inverse heat conduction problem
NASA Astrophysics Data System (ADS)
Pourgholi, R.; Azizi, N.; Gasimov, Y. S.; Aliev, F.; Khalafi, H. K.
2009-06-01
In this paper, we consider an inverse heat conduction problem (IHCP). A set of temperature measurements at a single sensor location inside the heat conduction body is required. Using a transformation, the ill-posed IHCP becomes a Cauchy problem. Since the solution of Cauchy problem, exists and is unique but not always stable, the ill-posed problem is closely approximated by a well-posed problem. For this new well-posed problem, the existence, uniqueness, and stability of the solution are proved.
Cameron, M.K.; Fomel, S.B.; Sethian, J.A.
2009-01-01
In the present work we derive and study a nonlinear elliptic PDE coming from the problem of estimation of sound speed inside the Earth. The physical setting of the PDE allows us to pose only a Cauchy problem, and hence is ill-posed. However we are still able to solve it numerically on a long enough time interval to be of practical use. We used two approaches. The first approach is a finite difference time-marching numerical scheme inspired by the Lax-Friedrichs method. The key features of this scheme is the Lax-Friedrichs averaging and the wide stencil in space. The second approach is a spectral Chebyshev method with truncated series. We show that our schemes work because of (1) the special input corresponding to a positive finite seismic velocity, (2) special initial conditions corresponding to the image rays, (3) the fact that our finite-difference scheme contains small error terms which damp the high harmonics; truncation of the Chebyshev series, and (4) the need to compute the solution only for a short interval of time. We test our numerical scheme on a collection of analytic examples and demonstrate a dramatic improvement in accuracy in the estimation of the sound speed inside the Earth in comparison with the conventional Dix inversion. Our test on the Marmousi example confirms the effectiveness of the proposed approach.
Interlocked optimization and fast gradient algorithm for a seismic inverse problem
Metivier, Ludovic
2011-08-10
Highlights: {yields} A 2D extension of the 1D nonlinear inversion of well-seismic data is given. {yields} Appropriate regularization yields a well-determined large scale inverse problem. {yields} An interlocked optimization loop acts as an efficient preconditioner. {yields} The adjoint state method is used to compute the misfit function gradient. {yields} Domain decomposition method yields an efficient parallel implementation. - Abstract: We give a nonlinear inverse method for seismic data recorded in a well from sources at several offsets from the borehole in a 2D acoustic framework. Given the velocity field, approximate values of the impedance are recovered. This is a 2D extension of the 1D inversion of vertical seismic profiles . The inverse problem generates a large scale undetermined ill-conditioned problem. Appropriate regularization terms render the problem well-determined. An interlocked optimization algorithm yields an efficient preconditioning. A gradient algorithm based on the adjoint state method and domain decomposition gives a fast parallel numerical method. For a realistic test case, convergence is attained in an acceptable time with 128 processors.
Celik, Hasan; Shaka, A J; Mandelshtam, V A
2010-09-01
We consider the harmonic inversion problem, and the associated spectral estimation problem, both of which are key numerical problems in NMR data analysis. Under certain conditions (in particular, in exact arithmetic) these problems have unique solutions. Therefore, these solutions must not depend on the inversion algorithm, as long as it is exact in principle. In this paper, we are not concerned with the algorithmic aspects of harmonic inversion, but rather with the sensitivity of the solutions of the problem to perturbations of the time-domain data. A sensitivity analysis was performed and the counterintuitive results call into question the common assumption made in "super-resolution" methods using non-uniform data sampling, namely, that the data should be sampled more often where the time signal has the largest signal-to-noise ratio. The numerical analysis herein demonstrates that the spectral parameters (such as the peak positions and amplitudes) resulting from the solution of the harmonic inversion problem are least susceptible to the perturbations in the values of data points at the edges of the time interval and most susceptible to the perturbations in the values at intermediate times. PMID:20663693
Ivanov, J.; Miller, R.D.; Xia, J.; Steeples, D.
2005-01-01
This paper is the second of a set of two papers in which we study the inverse refraction problem. The first paper, "Types of Geophysical Nonuniqueness through Minimization," studies and classifies the types of nonuniqueness that exist when solving inverse problems depending on the participation of a priori information required to obtain reliable solutions of inverse geophysical problems. In view of the classification developed, in this paper we study the type of nonuniqueness associated with the inverse refraction problem. An approach for obtaining a realistic solution to the inverse refraction problem is offered in a third paper that is in preparation. The nonuniqueness of the inverse refraction problem is examined by using a simple three-layer model. Like many other inverse geophysical problems, the inverse refraction problem does not have a unique solution. Conventionally, nonuniqueness is considered to be a result of insufficient data and/or error in the data, for any fixed number of model parameters. This study illustrates that even for overdetermined and error free data, nonlinear inverse refraction problems exhibit exact-data nonuniqueness, which further complicates the problem of nonuniqueness. By evaluating the nonuniqueness of the inverse refraction problem, this paper targets the improvement of refraction inversion algorithms, and as a result, the achievement of more realistic solutions. The nonuniqueness of the inverse refraction problem is examined initially by using a simple three-layer model. The observations and conclusions of the three-layer model nonuniqueness study are used to evaluate the nonuniqueness of more complicated n-layer models and multi-parameter cell models such as in refraction tomography. For any fixed number of model parameters, the inverse refraction problem exhibits continuous ranges of exact-data nonuniqueness. Such an unfavorable type of nonuniqueness can be uniquely solved only by providing abundant a priori information
Taming the non-linearity problem in GPR full-waveform inversion for high contrast media
NASA Astrophysics Data System (ADS)
Meles, Giovanni; Greenhalgh, Stewart; van der Kruk, Jan; Green, Alan; Maurer, Hansruedi
2012-03-01
We present a new algorithm for the inversion of full-waveform ground-penetrating radar (GPR) data. It is designed to tame the non-linearity issue that afflicts inverse scattering problems, especially in high contrast media. We first investigate the limitations of current full-waveform time-domain inversion schemes for GPR data and then introduce a much-improved approach based on a combined frequency-time-domain analysis. We show by means of several synthetic tests and theoretical considerations that local minima trapping (common in full bandwidth time-domain inversion) can be avoided by starting the inversion with only the low frequency content of the data. Resolution associated with the high frequencies can then be achieved by progressively expanding to wider bandwidths as the iterations proceed. Although based on a frequency analysis of the data, the new method is entirely implemented by means of a time-domain forward solver, thus combining the benefits of both frequency-domain (low frequency inversion conveys stability and avoids convergence to a local minimum; whereas high frequency inversion conveys resolution) and time-domain methods (simplicity of interpretation and recognition of events; ready availability of FDTD simulation tools).
Taming the non-linearity problem in GPR full-waveform inversion for high contrast media
NASA Astrophysics Data System (ADS)
Meles, Giovanni; Greenhalgh, Stewart; van der Kruk, Jan; Green, Alan; Maurer, Hansruedi
2011-02-01
We present a new algorithm for the inversion of full-waveform ground-penetrating radar (GPR) data. It is designed to tame the non-linearity issue that afflicts inverse scattering problems, especially in high contrast media. We first investigate the limitations of current full-waveform time-domain inversion schemes for GPR data and then introduce a much-improved approach based on a combined frequency-time-domain analysis. We show by means of several synthetic tests and theoretical considerations that local minima trapping (common in full bandwidth time-domain inversion) can be avoided by starting the inversion with only the low frequency content of the data. Resolution associated with the high frequencies can then be achieved by progressively expanding to wider bandwidths as the iterations proceed. Although based on a frequency analysis of the data, the new method is entirely implemented by means of a time-domain forward solver, thus combining the benefits of both frequency-domain (low frequency inversion conveys stability and avoids convergence to a local minimum; whereas high frequency inversion conveys resolution) and time-domain methods (simplicity of interpretation and recognition of events; ready availability of FDTD simulation tools).
Ivanov, J.; Miller, R.D.; Xia, J.; Steeples, D.; Park, C.B.
2006-01-01
We describe a possible solution to the inverse refraction-traveltime problem (IRTP) that reduces the range of possible solutions (nonuniqueness). This approach uses a reference model, derived from surface-wave shear-wave velocity estimates, as a constraint. The application of the joint analysis of refractions with surface waves (JARS) method provided a more realistic solution than the conventional refraction/tomography methods, which did not benefit from a reference model derived from real data. This confirmed our conclusion that the proposed method is an advancement in the IRTP analysis. The unique basic principles of the JARS method might be applicable to other inverse geophysical problems. ?? 2006 Society of Exploration Geophysicists.
NASA Astrophysics Data System (ADS)
Reiter, D. T.; Rodi, W. L.
2015-12-01
Constructing 3D Earth models through the joint inversion of large geophysical data sets presents numerous theoretical and practical challenges, especially when diverse types of data and model parameters are involved. Among the challenges are the computational complexity associated with large data and model vectors and the need to unify differing model parameterizations, forward modeling methods and regularization schemes within a common inversion framework. The challenges can be addressed in part by decomposing the inverse problem into smaller, simpler inverse problems that can be solved separately, providing one knows how to merge the separate inversion results into an optimal solution of the full problem. We have formulated an approach to the decomposition of large inverse problems based on the augmented Lagrangian technique from optimization theory. As commonly done, we define a solution to the full inverse problem as the Earth model minimizing an objective function motivated, for example, by a Bayesian inference formulation. Our decomposition approach recasts the minimization problem equivalently as the minimization of component objective functions, corresponding to specified data subsets, subject to the constraints that the minimizing models be equal. A standard optimization algorithm solves the resulting constrained minimization problems by alternating between the separate solution of the component problems and the updating of Lagrange multipliers that serve to steer the individual solution models toward a common model solving the full problem. We are applying our inversion method to the reconstruction of the·crust and upper-mantle seismic velocity structure across Eurasia.· Data for the inversion comprise a large set of P and S body-wave travel times·and fundamental and first-higher mode Rayleigh-wave group velocities.
NASA Astrophysics Data System (ADS)
Schuster, Thomas; Hofmann, Bernd; Kaltenbacher, Barbara
2012-10-01
Inverse problems can usually be modelled as operator equations in infinite-dimensional spaces with a forward operator acting between Hilbert or Banach spaces—a formulation which quite often also serves as the basis for defining and analyzing solution methods. The additional amount of structure and geometric interpretability provided by the concept of an inner product has rendered these methods amenable to a convergence analysis, a fact which has led to a rigorous and comprehensive study of regularization methods in Hilbert spaces over the last three decades. However, for numerous problems such as x-ray diffractometry, certain inverse scattering problems and a number of parameter identification problems in PDEs, the reasons for using a Hilbert space setting seem to be based on conventions rather than an appropriate and realistic model choice, so often a Banach space setting would be closer to reality. Furthermore, non-Hilbertian regularization and data fidelity terms incorporating a priori information on solution and noise, such as general Lp-norms, TV-type norms, or the Kullback-Leibler divergence, have recently become very popular. These facts have motivated intensive investigations on regularization methods in Banach spaces, a topic which has emerged as a highly active research field within the area of inverse problems. Meanwhile some of the most well-known regularization approaches, such as Tikhonov-type methods requiring the solution of extremal problems, and iterative ones like the Landweber method, the Gauss-Newton method, as well as the approximate inverse method, have been investigated for linear and nonlinear operator equations in Banach spaces. Convergence with rates has been proven and conditions on the solution smoothness and on the structure of nonlinearity have been formulated. Still, beyond the existing results a large number of challenging open questions have arisen, due to the more involved handling of general Banach spaces and the larger variety
NASA Astrophysics Data System (ADS)
Chmielewski, Arthur B.; Noca, Muriel; Ulvestad, James
2000-03-01
Supermassive black holes are among the most spectacular objects in the Universe, and are laboratories for physics in extreme conditions. Understanding the physics of massive black holes and related phenomena is a primary goal of the ARISE mission. The scientific goals of the mission are described in detail on the ARISE web site http://arise.ipl.nasa.gov and in the ARISE Science Goals document. The following paper, as the title suggests, is not intended to be a comprehensive description of ARISE, but deals only with one aspect of the ARISE mission-the inflatable antenna which is the key element of the ARISE spacecraft. This spacecraft,due to the extensive reliance on inflatables, may be considered as the first generation Gossamer spacecraft
A numerical study of the inverse problem of breast infrared thermography modeling
NASA Astrophysics Data System (ADS)
Jiang, Li; Zhan, Wang; Loew, Murray H.
2010-03-01
Infrared thermography has been shown to be a useful adjunctive tool for breast cancer detection. Previous thermography modeling techniques generally dealt with the "forward problem", i.e., to estimate the breast thermogram from known properties of breast tissues. The present study aims to deal with the so-called "inverse problem", namely to estimate the thermal properties of the breast tissues from the observed surface temperature distribution. By comparison, the inverse problem is a more direct way of interpreting a breast thermogram for specific physiological and/or pathological information. In tumor detection, for example, it is particularly important to estimate the tumor-induced thermal contrast, even though the corresponding non-tumor thermal background usually is unknown due to the difficulty of measuring the individual thermal properties. Inverse problem solving is technically challenging due to its ill-posed nature, which is evident primarily by its sensitivity to imaging noise. Taking advantage of our previously developed forward-problemsolving techniques with comprehensive thermal-elastic modeling, we examine here the feasibility of solving the inverse problem of the breast thermography. The approach is based on a presumed spatial constraint applied to three major thermal properties, i.e., thermal conductivity, blood perfusion, and metabolic heat generation, for each breast tissue type. Our results indicate that the proposed inverse-problem-solving scheme can be numerically stable under imaging noise of SNR ranging 32 ~ 40 dB, and that the proposed techniques can be effectively used to improve the estimation to the tumor-induced thermal contrast, especially for smaller and deeper tumors.
Modeling Direct and Inverse Problems in Ferritic Heat-Exchanger Tubes
NASA Astrophysics Data System (ADS)
Sabbagh, Harold A.; Murphy, R. Kim; Sabbagh, Elias H.; Aldrin, John C.
2011-06-01
In this paper, we present examples of direct and inverse problems involving ferritic tubes, using the proprietary eddy-current NDE code, VIC-3D©. We demonstrate conditions that are peculiar to ferritic tubes, and give insight into the optimum methods for characterizing the tubes and flaws within them.
Inverse problem for the Verhulst equation of limited population growth with discrete experiment data
NASA Astrophysics Data System (ADS)
Azimov, Anvar; Kasenov, Syrym; Nurseitov, Daniyar; Serovajsky, Simon
2016-08-01
Verhulst limited growth model with unknown parameters of growth is considered. These parameters are defined by discrete experiment data. This inverse problem is solved with using gradient method with interpolation of data and without it. Approximation of the delta-function is used for the latter case. As an example the bacteria population E.coli is considered.
Global stability for an inverse problem in soil–structure interaction
Alessandrini, G.; Morassi, A.; Rosset, E.; Vessella, S.
2015-01-01
We consider the inverse problem of determining the Winkler subgrade reaction coefficient of a slab foundation modelled as a thin elastic plate clamped at the boundary. The plate is loaded by a concentrated force and its transversal deflection is measured at the interior points. We prove a global Hölder stability estimate under (mild) regularity assumptions on the unknown coefficient. PMID:26345082
ON THE GEOSTATISTICAL APPROACH TO THE INVERSE PROBLEM. (R825689C037)
The geostatistical approach to the inverse problem is discussed with emphasis on the importance of structural analysis. Although the geostatistical approach is occasionally misconstrued as mere cokriging, in fact it consists of two steps: estimation of statist...
NASA Astrophysics Data System (ADS)
Bidaibekov, Yessen Y.; Kornilov, Viktor S.; Kamalova, Guldina B.; Akimzhan, Nagima Sh.
2015-09-01
Methodical aspects of teaching students of higher educational institutions of natural science orientations of training of inverse problems for differential equations are considered in the article. A fact that an academic knowledge and competence in the field of applied mathematics is formed during such training is taken into consideration.
Integro-differential method of solving the inverse coefficient heat conduction problem
NASA Astrophysics Data System (ADS)
Baranov, V. L.; Zasyad'Ko, A. A.; Frolov, G. A.
2010-03-01
On the basis of differential transformations, a stable integro-differential method of solving the inverse heat conduction problem is suggested. The method has been tested on the example of determining the thermal diffusivity on quasi-stationary fusion and heating of a quartz glazed ceramics specimen.
Jiang, Mingfeng; Xia, Ling; Huang, Wenqing; Shou, Guofa; Liu, Feng; Crozier, Stuart
2009-10-01
Regularization is an effective method for the solution of ill-posed ECG inverse problems, such as computing epicardial potentials from body surface potentials. The aim of this work was to explore more robust regularization-based solutions through the application of subspace preconditioned LSQR (SP-LSQR) to the study of model-based ECG inverse problems. Here, we presented three different subspace splitting methods, i.e., SVD, wavelet transform and cosine transform schemes, to the design of the preconditioners for ill-posed problems, and to evaluate the performance of algorithms using a realistic heart-torso model simulation protocol. The results demonstrated that when compared with the LSQR, LSQR-Tik and Tik-LSQR method, the SP-LSQR produced higher efficiency and reconstructed more accurate epcicardial potential distributions. Amongst the three applied subspace splitting schemes, the SVD-based preconditioner yielded the best convergence rate and outperformed the other two in seeking the inverse solutions. Moreover, when optimized by the genetic algorithms (GA), the performances of SP-LSQR method were enhanced. The results from this investigation suggested that the SP-LSQR was a useful regularization technique for cardiac inverse problems. PMID:19564127
Numerical solution of 2D-vector tomography problem using the method of approximate inverse
NASA Astrophysics Data System (ADS)
Svetov, Ivan; Maltseva, Svetlana; Polyakova, Anna
2016-08-01
We propose a numerical solution of reconstruction problem of a two-dimensional vector field in a unit disk from the known values of the longitudinal and transverse ray transforms. The algorithm is based on the method of approximate inverse. Numerical simulations confirm that the proposed method yields good results of reconstruction of vector fields.
Variable-permittivity linear inverse problem for the H(sub z)-polarized case
NASA Technical Reports Server (NTRS)
Moghaddam, M.; Chew, W. C.
1993-01-01
The H(sub z)-polarized inverse problem has rarely been studied before due to the complicated way in which the unknown permittivity appears in the wave equation. This problem is equivalent to the acoustic inverse problem with variable density. We have recently reported the solution to the nonlinear variable-permittivity H(sub z)-polarized inverse problem using the Born iterative method. Here, the linear inverse problem is solved for permittivity (epsilon) and permeability (mu) using a different approach which is an extension of the basic ideas of diffraction tomography (DT). The key to solving this problem is to utilize frequency diversity to obtain the required independent measurements. The receivers are assumed to be in the far field of the object, and plane wave incidence is also assumed. It is assumed that the scatterer is weak, so that the Born approximation can be used to arrive at a relationship between the measured pressure field and two terms related to the spatial Fourier transform of the two unknowns, epsilon and mu. The term involving permeability corresponds to monopole scattering and that for permittivity to dipole scattering. Measurements at several frequencies are used and a least squares problem is solved to reconstruct epsilon and mu. It is observed that the low spatial frequencies in the spectra of epsilon and mu produce inaccuracies in the results. Hence, a regularization method is devised to remove this problem. Several results are shown. Low contrast objects for which the above analysis holds are used to show that good reconstructions are obtained for both permittivity and permeability after regularization is applied.
Solution of inverse problem of fuzzy relational equation by using perceptron model
NASA Technical Reports Server (NTRS)
Hirota, Kaoru; Ikoma, Norikazu
1991-01-01
A Max-Min fuzzy system can be regarded as a network of max and min operational elements. Thus, the inverse problem of a fuzzy relational equation is interpreted as an input estimation problem from output values in the corresponding network. An approximate network model of a fuzzy relational system is proposed. An algorithm for obtaining an approximate solution of the system is presented for using a neural network technique.
NASA Astrophysics Data System (ADS)
Orazov, Isabek; Sadybekov, Makhmud A.
2015-09-01
In this paper, we consider one family of problems simulating the determination of target components and density of sources from given values of the initial and final states. The mathematical statement of these problems leads to the inverse problem for the diffusion equation, where it is required to find not only a solution of the problem, but also its right-hand side that depends only on a spatial variable. One of specific features of the considered problems is that the system of eigenfunctions of the multiple differentiation operator subject to boundary conditions of the initial problem does not have the basis property. The other specific feature of the considered problems is that an unknown function is simultaneously present both in the right-hand side of the equation and in conditions of the initial and final redefinition. We prove the unique existence of a generalized solution to the mentioned problem.
Resolution Analysis for Experiment Planning of a Nonlinear Seafloor Acoustic Inverse Problem
NASA Astrophysics Data System (ADS)
Ganse, A. A.; Odom, R. I.
2007-12-01
Geoacoustic inversion is the estimation of physical properties of the ocean bottom as a continuous function of position (or depth) in the seafloor given acoustic receptions in the water column. It is closely related to marine reflection seismology but also has features of refraction seismology, and uses sonar equipment and less than ideal geometries because accurate scientific determination of the seafloor may not be the primary goal of the experiments. However, the authors show how a pre-measurement inverse theory resolution analysis can be used as part of experiment planning regarding sensor placement and ship tracks, such that a desire for an experimental configuration giving the most information in bottom inversion can be quantitatively balanced with that for other needs like tracking and communication. This nonlinear geoacoustic inverse problem is ill-posed, so that one can only estimate the continuous function of seafloor properties to a limited resolution. This limited resolution varies with experiment geometry, frequency, and other such factors, and can be quantified in either a frequentist or Bayesian framework. Given statistics of the measurement noise (but without any new measurements themselves), the resolution can be quantified exactly for a linear inverse problem, and compared between different experiment geometries. Nonlinear problems complicate this picture, but if the problem can be transformed into a weakly nonlinear form then the resolution may still be explored in an approximate sense and used as a tool in the planning phase. The ideal situation is when previous seafloor estimates exist for the same region in which a new experiment with new geometry and configuration is being planned. For the scenario without previous results, a somewhat more ad-hoc approach can still compare changes in resolution across different seafloor models. This presentation demonstrates the technique for a synthetic problem involving a single stationary source and a
FOREWORD: 3rd International Workshop on New Computational Methods for Inverse Problems (NCMIP 2013)
NASA Astrophysics Data System (ADS)
Blanc-Féraud, Laure; Joubert, Pierre-Yves
2013-10-01
Conference logo This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 3rd International Workshop on New Computational Methods for Inverse Problems, NCMIP 2013 (http://www.farman.ens-cachan.fr/NCMIP_2013.html). This workshop took place at Ecole Normale Supérieure de Cachan, in Cachan, France, on 22 May 2013, at the initiative of Institut Farman. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of the ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/), and secondly at the initiative of Institut Farman, in May 2012 (http://www.farman.ens-cachan.fr/NCMIP_2012.html). The NCMIP Workshop focused on recent advances in the resolution of inverse problems. Indeed inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational
FOREWORD: 2nd International Workshop on New Computational Methods for Inverse Problems (NCMIP 2012)
NASA Astrophysics Data System (ADS)
Blanc-Féraud, Laure; Joubert, Pierre-Yves
2012-09-01
Conference logo This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 2nd International Workshop on New Computational Methods for Inverse Problems, (NCMIP 2012). This workshop took place at Ecole Normale Supérieure de Cachan, in Cachan, France, on 15 May 2012, at the initiative of Institut Farman. The first edition of NCMIP also took place in Cachan, France, within the scope of the ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/). The NCMIP Workshop focused on recent advances in the resolution of inverse problems. Indeed inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finance. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, kernel methods, learning methods, convex optimization, free discontinuity problems, metamodels, proper orthogonal decomposition
Analysis of forward and inverse problems in chemical dynamics and spectroscopy
Rabitz, H.
1993-12-01
The overall scope of this research concerns the development and application of forward and inverse analysis tools for problems in chemical dynamics and chemical kinetics. The chemical dynamics work is specifically associated with relating features in potential surfaces and resultant dynamical behavior. The analogous inverse research aims to provide stable algorithms for extracting potential surfaces from laboratory data. In the case of chemical kinetics, the focus is on the development of systematic means to reduce the complexity of chemical kinetic models. Recent progress in these directions is summarized below.
NASA Astrophysics Data System (ADS)
Fu, Jing-Li; Fu, Li-Ping; Chen, Ben-Yong; Sun, Yi
2016-01-01
This letter focuses on studying Lie symmetries and their inverse problems of the fractional nonholonomic Hamilton systems. Based on the invariance of the fractional motion equations, constraint equations and virtual displacement restrictive conditions of the systems under the infinitesimal transformation with respect to the time and generalized coordinates, the Lie symmetries and conserved quantities of the fractional nonholonomic Hamilton system are discussed and the corresponding definitions, determining equations, limiting equations, additional restricting equations and Lie theorems are given. The letter also systematically studies inverse theorems of Lie symmetries of the fractional nonholonomic Hamilton systems. Finally, an example is discussed to illustrate theses results.
An inverse time-dependent source problem for a time-fractional diffusion equation
NASA Astrophysics Data System (ADS)
Wei, T.; Li, X. L.; Li, Y. S.
2016-08-01
This paper is devoted to identifying a time-dependent source term in a multi-dimensional time-fractional diffusion equation from boundary Cauchy data. The existence and uniqueness of a strong solution for the corresponding direct problem with homogeneous Neumann boundary condition are firstly proved. We provide the uniqueness and a stability estimate for the inverse time-dependent source problem. Then we use the Tikhonov regularization method to solve the inverse source problem and propose a conjugate gradient algorithm to find a good approximation to the minimizer of the Tikhonov regularization functional. Numerical examples in one-dimensional and two-dimensional cases are provided to show the effectiveness of the proposed method. This paper was supported by the NSF of China (11371181) and the Fundamental Research Funds for the Central Universities (lzujbky-2013-k02).
NASA Astrophysics Data System (ADS)
Kirsch, Andreas; Rieder, Andreas
2016-08-01
It is common knowledge—mainly based on experience—that parameter identification problems in partial differential equations are ill-posed. Yet, a mathematical sound argumentation is missing, except for some special cases. We present a general theory for inverse problems related to abstract evolution equations which explains not only their local ill-posedness but also provides the Fréchet derivative and its adjoint of the corresponding parameter-to-solution map which are needed, e.g., in Newton-like solvers. Our abstract results are applied to inverse problems related to the following first order hyperbolic systems: Maxwell’s equation (electromagnetic scattering in conducting media) and elastic wave equation (seismic imaging).
NASA Astrophysics Data System (ADS)
Gong, Xing; Wang, Yuanmei
2001-09-01
A neural network approach to the inverse scattering problem for microwave tomographic reconstruction is presented. Although the technique of microwave tomography has been developed for more than two decades, it is still in its infancy in that it is necessary to solve the inverse scattering problem, which is well known as an ill-posed and nonlinear problem and therefore difficult to deal with. To improve the inherent ill-posedness of the problem, good regularization procedures are required. In this paper, an edge-preserving regularization is proposed with a set of line processes to preserve the edge of the reconstructed image. Since the unknown dielectric permittivities are continuous complex variables and the line processes are binary variables, an augmented Hopfield network is applied to the mixed-variable optimization problem. With this method, a priori knowledge can be conveniently incorporated into the optimization process, and inversions of large matrices are avoided. A numerical example of a simple model illuminated by the transverse magnetic incident waves is reported, and the advantages and limitations of the method are discussed.
Inverse problems and optimal experiment design in unsteady heat transfer processes identification
NASA Technical Reports Server (NTRS)
Artyukhin, Eugene A.
1991-01-01
Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.
NASA Astrophysics Data System (ADS)
Gross, Lutz; Altinay, Cihan; Fenwick, Joel; Smith, Troy
2014-05-01
The program package escript has been designed for solving mathematical modeling problems using python, see Gross et al. (2013). Its development and maintenance has been funded by the Australian Commonwealth to provide open source software infrastructure for the Australian Earth Science community (recent funding by the Australian Geophysical Observing System EIF (AGOS) and the AuScope Collaborative Research Infrastructure Scheme (CRIS)). The key concepts of escript are based on the terminology of spatial functions and partial differential equations (PDEs) - an approach providing abstraction from the underlying spatial discretization method (i.e. the finite element method (FEM)). This feature presents a programming environment to the user which is easy to use even for complex models. Due to the fact that implementations are independent from data structures simulations are easily portable across desktop computers and scalable compute clusters without modifications to the program code. escript has been successfully applied in a variety of applications including modeling mantel convection, melting processes, volcanic flow, earthquakes, faulting, multi-phase flow, block caving and mineralization (see Poulet et al. 2013). The recent escript release (see Gross et al. (2013)) provides an open framework for solving joint inversion problems for geophysical data sets (potential field, seismic and electro-magnetic). The strategy bases on the idea to formulate the inversion problem as an optimization problem with PDE constraints where the cost function is defined by the data defect and the regularization term for the rock properties, see Gross & Kemp (2013). This approach of first-optimize-then-discretize avoids the assemblage of the - in general- dense sensitivity matrix as used in conventional approaches where discrete programming techniques are applied to the discretized problem (first-discretize-then-optimize). In this paper we will discuss the mathematical framework for
Investigation of one inverse problem in case of modeling water areas with "liquid" boundaries
NASA Astrophysics Data System (ADS)
Sheloput, Tatiana; Agoshkov, Valery
2015-04-01
In hydrodynamics often appears the problem of modeling water areas (oceans, seas, rivers, etc.) with "liquid" boundaries. "Liquid" boundary means set of those parts of boundary where impermeability condition is broken (for example, straits, bays borders, estuaries, interfaces of oceans). Frequently such effects are ignored: for "liquid" boundaries the same conditions are used as for "solid" ones, "material boundary" approximation is applied [1]. Sometimes it is possible to interpolate the results received from models of bigger areas. Moreover, approximate estimates for boundary conditions are often used. However, those approximations are not always valid. Sometimes errors in boundary condition determination could lead to a significant decrease in the accuracy of the simulation results. In this work one way of considering the problem mentioned above is described. According to this way one inverse problem on reconstruction of boundary function in convection-reaction-diffusion equations which describe transfer of heat and salinity is solved. The work is based on theory of adjoint equations [2] and optimal control, as well as on common methodology of investigation inverse problems [3]. The work contains theoretical investigation and the results of computer simulation applied for the Baltic Sea. Moreover, conditions and restrictions that should be satisfied for solvability of the problem are entered and justified in the work. Submitted work could be applied for the solution of more complicated inverse problems and data assimilation problems in the areas with "liquid" boundaries; also it is a step for developing algorithms on computing level, speed, temperature and salinity that could be applied for real objects. References 1. A. E. Gill. Atmosphere-ocean dynamics. // London: Academic Press, 1982. 2. G. I. Marchuk. Adjoint equations. // Moscow: INM RAS, 2000, 175 p. (in Russian). 3. V.I. Agoshkov. The methods of optimal control and adjoint equations in problems of
Geometric structure in seismic data, sampling and computational inverse problems (Invited)
NASA Astrophysics Data System (ADS)
De Hoop, M. V.; Andersson, F.; Tricoche, X.; Xia, J.; Yeh, R.
2013-12-01
In (nonlinear) seismic inverse problems one requires data from available large arrays acquired for many events. These data, naturally, contain information about Earth's interior over a large range of scales. From the viewpoint of detecting broadband wavefields, however, sampling often remains a challenge. We discuss various concepts and techniques which aid in analyzing and partitioning (or decomposing) such data, including compression with a novel, multi-scale, frame of Gaussian wavepackets, graph cuts and (extended) structure tensors. We emphasize the underlying geometric structure both in the physical embedding and in the associated space where the data find a natural representation. We consider several dimensionality reduction approaches including manifold learning, and develop possible notions of distance. These enable the development of methods of comparing data, for example, within and between arrays, and comparing observed with simulated data for inverse problems and regularization. Within the context of inverse problems we briefly mention the synthesis of the Dirichlet-to-Neumann map from the data, randomized sampling and direct structured solvers of the direct problem using hierarchically semi-separable (HSS) matrices in a multi-frequency formulation enabling an efficient workflow with a large number of events.
Group-sparsity regularization for ill-posed subsurface flow inverse problems
NASA Astrophysics Data System (ADS)
Golmohammadi, Azarang; Khaninezhad, Mohammad-Reza M.; Jafarpour, Behnam
2015-10-01
Sparse representations provide a flexible and parsimonious description of high-dimensional model parameters for reconstructing subsurface flow property distributions from limited data. To further constrain ill-posed inverse problems, group-sparsity regularization can take advantage of possible relations among the entries of unknown sparse parameters when: (i) groups of sparse elements are either collectively active or inactive and (ii) only a small subset of the groups is needed to approximate the parameters of interest. Since subsurface properties exhibit strong spatial connectivity patterns they may lead to sparse descriptions that satisfy the above conditions. When these conditions are established, a group-sparsity regularization can be invoked to facilitate the solution of the resulting inverse problem by promoting sparsity across the groups. The proposed regularization penalizes the number of groups that are active without promoting sparsity within each group. Two implementations are presented in this paper: one based on the multiresolution tree structure of Wavelet decomposition, without a need for explicit prior models, and another learned from explicit prior model realizations using sparse principal component analysis (SPCA). In each case, the approach first classifies the parameters of the inverse problem into groups with specific connectivity features, and then takes advantage of the grouped structure to recover the relevant patterns in the solution from the flow data. Several numerical experiments are presented to demonstrate the advantages of additional constraining power of group-sparsity in solving ill-posed subsurface model calibration problems.
A hierarchical Bayesian-MAP approach to inverse problems in imaging
NASA Astrophysics Data System (ADS)
Raj, Raghu G.
2016-07-01
We present a novel approach to inverse problems in imaging based on a hierarchical Bayesian-MAP (HB-MAP) formulation. In this paper we specifically focus on the difficult and basic inverse problem of multi-sensor (tomographic) imaging wherein the source object of interest is viewed from multiple directions by independent sensors. Given the measurements recorded by these sensors, the problem is to reconstruct the image (of the object) with a high degree of fidelity. We employ a probabilistic graphical modeling extension of the compound Gaussian distribution as a global image prior into a hierarchical Bayesian inference procedure. Since the prior employed by our HB-MAP algorithm is general enough to subsume a wide class of priors including those typically employed in compressive sensing (CS) algorithms, HB-MAP algorithm offers a vehicle to extend the capabilities of current CS algorithms to include truly global priors. After rigorously deriving the regression algorithm for solving our inverse problem from first principles, we demonstrate the performance of the HB-MAP algorithm on Monte Carlo trials and on real empirical data (natural scenes). In all cases we find that our algorithm outperforms previous approaches in the literature including filtered back-projection and a variety of state-of-the-art CS algorithms. We conclude with directions of future research emanating from this work.
Variational approach to direct and inverse problems of atmospheric pollution studies
NASA Astrophysics Data System (ADS)
Penenko, Vladimir; Tsvetova, Elena; Penenko, Alexey
2016-04-01
We present the development of a variational approach for solving interrelated problems of atmospheric hydrodynamics and chemistry concerning air pollution transport and transformations. The proposed approach allows us to carry out complex studies of different-scale physical and chemical processes using the methods of direct and inverse modeling [1-3]. We formulate the problems of risk/vulnerability and uncertainty assessment, sensitivity studies, variational data assimilation procedures [4], etc. A computational technology of constructing consistent mathematical models and methods of their numerical implementation is based on the variational principle in the weak constraint formulation specifically designed to account for uncertainties in models and observations. Algorithms for direct and inverse modeling are designed with the use of global and local adjoint problems. Implementing the idea of adjoint integrating factors provides unconditionally monotone and stable discrete-analytic approximations for convection-diffusion-reaction problems [5,6]. The general framework is applied to the direct and inverse problems for the models of transport and transformation of pollutants in Siberian and Arctic regions. The work has been partially supported by the RFBR grant 14-01-00125 and RAS Presidium Program I.33P. References: 1. V. Penenko, A.Baklanov, E. Tsvetova and A. Mahura . Direct and inverse problems in a variational concept of environmental modeling //Pure and Applied Geoph.(2012) v.169: 447-465. 2. V. V. Penenko, E. A. Tsvetova, and A. V. Penenko Development of variational approach for direct and inverse problems of atmospheric hydrodynamics and chemistry, Izvestiya, Atmospheric and Oceanic Physics, 2015, Vol. 51, No. 3, p. 311-319, DOI: 10.1134/S0001433815030093. 3. V.V. Penenko, E.A. Tsvetova, A.V. Penenko. Methods based on the joint use of models and observational data in the framework of variational approach to forecasting weather and atmospheric composition
Inverse problem for shape control of flexible space reflectors using distributed solar pressure
NASA Astrophysics Data System (ADS)
Borggräfe, A.; Heiligers, J.; Ceriotti, M.; McInnes, C. R.
2014-07-01
This paper investigates controlled elastic deflection of thin circular space reflectors using an inverse problem approach to non-linear thin membrane theory. When changing the surface reflectivity across the membrane, the distributed loads due to ambient solar radiation pressure can be manipulated optically, thus controlling the surface shape without using mechanical or piezo-electric systems. The surface reflectivity can in principle be modulated using uniformly distributed thin-film electro-chromic coatings. We present an analytic solution to the inverse problem of finding the necessary reflectivity distribution that creates a specific membrane deflection, for example that of a parabolic reflector. Importantly, the reflectivity distribution across the surface is found to be independent of membrane size, thickness and solar distance, enabling engineering of the reflectivity distribution directly during the manufacture of the membrane.
NASA Astrophysics Data System (ADS)
Yang, Ping; Feng, Xue-Wen; Liang, Wen-Jun; Wu, Kai-Su
2015-02-01
It is the main aim of this paper to investigate the numerical solutions of the inverse black body radiation problems. The inverse black body radiation problem is ill-posed. Using Gaussian-Laguerre integral formula which is a higher accuracy numerical integration formula with less node numbers to approximate the integral item of black body radiation equation, the black radiation equation is converted into a group of lower dimension algebraic equations. To solve the lower dimension algebraic equation, it only needs to use common Tikhonov regularization methods. The regularization parameter is chosen by using L-curve. Our method reduces the complexity of the algorithm, so the operability of our method is enhanced. Numerical results show that our algorithm is simple and effective, and has better calculation accuracy at the same time.
A multiple-scale Pascal polynomial for 2D Stokes and inverse Cauchy-Stokes problems
NASA Astrophysics Data System (ADS)
Liu, Chein-Shan; Young, D. L.
2016-05-01
The polynomial expansion method is a useful tool for solving both the direct and inverse Stokes problems, which together with the pointwise collocation technique is easy to derive the algebraic equations for satisfying the Stokes differential equations and the specified boundary conditions. In this paper we propose two novel numerical algorithms, based on a third-first order system and a third-third order system, to solve the direct and the inverse Cauchy problems in Stokes flows by developing a multiple-scale Pascal polynomial method, of which the scales are determined a priori by the collocation points. To assess the performance through numerical experiments, we find that the multiple-scale Pascal polynomial expansion method (MSPEM) is accurate and stable against large noise.
NASA Astrophysics Data System (ADS)
Terekhoff, Serge A.
1997-04-01
This paper summarizes theoretical findings and applications of artificial neural networks to modeling of complex engineered system response in the abnormal environments. The thermal fire impact on the industrial container for waste and fissile materials was investigated using model and experimental data. Solutions for the direct problem show that the generalization properties of neural network based model are significantly better than those for standard interpolation methods. Minimal amount of data required for good prediction of system response is estimated in computer experiments with MLP network. It was shown that Kohonen's self-organizing map with counterpropagation may also estimate local accuracy of regularized solution for inverse and combined problems. Feature space regions of partial correctness of the inverse model can be automatically extracted using adaptive clustering. Practical findings include time strategy recommendations for fire-safe services when industrial or transport accidents occur.
Trans-dimensional Monte Carlo sampling applied to the magnetotelluric inverse problem
NASA Astrophysics Data System (ADS)
Mandolesi, Eric; Piana Agostinetti, Nicola
2015-01-01
The data required to build geological models of the subsurface are often unavailable from direct measurements or well logs. In order to image the subsurface geological structures several geophysical methods have been developed. The magnetotelluric (MT) method uses natural, time-varying electromagnetic (EM) fields as its source to measure the EM impedance of the subsurface. The interpretation of these data is routinely undertaken by solving inverse problems to produce 1D, 2D or 3D electrical conductivity models of the subsurface. In classical MT inverse problems the investigated models are parametrized using a fixed number of unknowns (i.e. fixed number of layers in a 1D model, or a fixed number of cells in a 2D model), and the non-uniqueness of the solution is handled by a regularization term added to the objective function. This study presents a different approach to the 1D MT inverse problem, by using a trans-dimensional Monte Carlo sampling algorithm, where trans-dimensionality implies that the number of unknown parameters is a parameter itself. This construction has been shown to have a built-in Occam razor, so that the regularization term is not required to produce a simple model. The influences of subjective choices in the interpretation process can therefore be sensibly reduced. The inverse problem is solved within a Bayesian framework, where posterior probability distribution of the investigated parameters are sought, rather than a single best-fit model, and uncertainties on the model parameters, and their correlation, can be easily measured.
An inverse source problem for a variable speed wave equation with discrete-in-time sources
NASA Astrophysics Data System (ADS)
de Hoop, Maarten V.; Tittelfitz, Justin
2015-07-01
We consider a time-dependent inverse source problem for the inhomogeneous wave equation, with sources that are delta-like in time, and (roughly speaking) non-negative and of limited oscillation in space. We take the displacement of the wave on a fixed detection surface, over some time interval, as our data. We then use time-reversal from the boundary to generate a related waveform, and discuss how to identify sources (as well as artifacts) from this.
Adaptive Thouless-Anderson-Palmer approach to inverse Ising problems with quenched random fields.
Huang, Haiping; Kabashima, Yoshiyuki
2013-06-01
The adaptive Thouless-Anderson-Palmer equation is derived for inverse Ising problems in the presence of quenched random fields. We test the proposed scheme on Sherrington-Kirkpatrick, Hopfield, and random orthogonal models and find that the adaptive Thouless-Anderson-Palmer approach allows accurate inference of quenched random fields whose distribution can be either Gaussian or bimodal. In particular, another competitive method for inferring external fields, namely, the naive mean field method with diagonal weights, is compared and discussed. PMID:23848649
NASA Astrophysics Data System (ADS)
Koepke, C.; Irving, J.; Roubinet, D.
2014-12-01
Geophysical methods have gained much interest in hydrology over the past two decades because of their ability to provide estimates of the spatial distribution of subsurface properties at a scale that is often relevant to key hydrological processes. Because of an increased desire to quantify uncertainty in hydrological predictions, many hydrogeophysical inverse problems have recently been posed within a Bayesian framework, such that estimates of hydrological properties and their corresponding uncertainties can be obtained. With the Bayesian approach, it is often necessary to make significant approximations to the associated hydrological and geophysical forward models such that stochastic sampling from the posterior distribution, for example using Markov-chain-Monte-Carlo (MCMC) methods, is computationally feasible. These approximations lead to model structural errors, which, so far, have not been properly treated in hydrogeophysical inverse problems. Here, we study the inverse problem of estimating unsaturated hydraulic properties, namely the van Genuchten-Mualem (VGM) parameters, in a layered subsurface from time-lapse, zero-offset-profile (ZOP) ground penetrating radar (GPR) data, collected over the course of an infiltration experiment. In particular, we investigate the effects of assumptions made for computational tractability of the stochastic inversion on model prediction errors as a function of depth and time. These assumptions are that (i) infiltration is purely vertical and can be modeled by the 1D Richards equation, and (ii) the petrophysical relationship between water content and relative dielectric permittivity is known. Results indicate that model errors for this problem are far from Gaussian and independently identically distributed, which has been the common assumption in previous efforts in this domain. In order to develop a more appropriate likelihood formulation, we use (i) a stochastic description of the model error that is obtained through
Uniqueness of the interior plane strain time-harmonic viscoelastic inverse problem
NASA Astrophysics Data System (ADS)
Zhang, Yixiao; Barbone, Paul E.; Harari, Isaac; Oberai, Assad A.
2016-07-01
Elasticity imaging has emerged as a promising medical imaging technique with applications in the detection, diagnosis and treatment monitoring of several types of disease. In elasticity imaging measured displacement fields are used to generate images of elastic parameters of tissue by solving an inverse problem. When the tissue excitation, and the resulting tissue motion is time-harmonic, elasticity imaging can be extended to image the viscoelastic properties of the tissue. This leads to an inverse problem for the complex-valued shear modulus at a given frequency. In this manuscript we have considered the uniqueness of this inverse problem for an incompressible, isotropic linear viscoelastic solid in a state of plane strain. For a single measured displacement field we conclude that the solution is infinite dimensional, and the data required to render it unique is determined by the measured strain field. In contrast, for two independent displacement fields such that the principal directions of the resulting strain fields are different, the space of possible solutions is eight dimensional, and given additional data, like the value of the shear modulus at four locations, or over a calibration region, we may determine the shear modulus everywhere. We have also considered simple analytical examples that verify these results and offer additional insights. The results derived in this paper may be used as guidelines by the practitioners of elasticity imaging in designing more robust and accurate imaging protocols.
Stepnowski, A; Moszyński, M
2000-05-01
In situ indirect methods of fish target strength (TS) estimation are analyzed in terms of the inverse techniques recently applied to the problem in question. The solution of this problem requires finding the unknown probability density function (pdf) of fish target strength from acoustic echoes, which can be estimated by solving the integral equation, relating pdf's of echo variable, target strength, and beam pattern of the echosounder transducer. In the first part of the paper the review of existing indirect in situ TS-estimation methods is presented. The second part introduces the novel TS-estimation methods, viz.: Expectation, Maximization, and Smoothing (EMS), Windowed Singular Value Decomposition (WSVD), Regularization and Wavelet Decomposition, which are compared using simulations as well as actual data from acoustic surveys. The survey data, acquired by the dual-beam digital echosounder, were thoroughly analyzed by numerical algorithms and the target strength and acoustical backscattering length pdf's estimates were calculated from fish echoes received in the narrow beam channel of the echosounder. Simultaneously, the estimates obtained directly from the dual-beam system were used as a reference for comparison of the estimates calculated by the newly introduced inverse techniques. The TS estimates analyzed in the paper are superior to those obtained from deconvolution or other conventional techniques, as the newly introduced methods partly avoid the problem of ill-conditioned equations and matrix inversion. PMID:10830379
NASA Astrophysics Data System (ADS)
Oware, E. K.; Moysey, S. M. J.; Khan, T.
2013-10-01
We introduce a new strategy for integrating hydrologic process information as a constraint within hydrogeophysical imaging problems. The approach uses a basis-constrained inversion where basis vectors are tuned to the hydrologic problem of interest. Tuning is achieved using proper orthogonal decomposition (POD) to extract an optimal basis from synthetic training data generated by Monte Carlo simulations representative of hydrologic processes at a site. A synthetic case study illustrates that the approach performs well relative to other common inversion strategies for imaging a solute plume using an electrical resistivity survey, even when the initial conceptualization of hydrologic processes is incorrect. In two synthetic case studies, we found that the POD approach was able to significantly improve imaging of the plume by reducing the root mean square error of the concentration estimates by a factor of two. More importantly, the POD approach was able to better capture the bimodal nature of the plume in the second case study, even though the prior conceptual model for the POD basis was for a single plume. The ability of the POD inversion to improve concentration estimates exemplifies the importance of integrating process information within geophysical imaging problems. In contrast, the ability to capture the bimodality of the plume in the second example indicates the flexibility of the technique to move away from this prior process constraint when it is inconsistent with the observed ERI data.
NASA Astrophysics Data System (ADS)
Ma, Xiang; Zabaras, Nicholas
2009-03-01
A new approach to modeling inverse problems using a Bayesian inference method is introduced. The Bayesian approach considers the unknown parameters as random variables and seeks the probabilistic distribution of the unknowns. By introducing the concept of the stochastic prior state space to the Bayesian formulation, we reformulate the deterministic forward problem as a stochastic one. The adaptive hierarchical sparse grid collocation (ASGC) method is used for constructing an interpolant to the solution of the forward model in this prior space which is large enough to capture all the variability/uncertainty in the posterior distribution of the unknown parameters. This solution can be considered as a function of the random unknowns and serves as a stochastic surrogate model for the likelihood calculation. Hierarchical Bayesian formulation is used to derive the posterior probability density function (PPDF). The spatial model is represented as a convolution of a smooth kernel and a Markov random field. The state space of the PPDF is explored using Markov chain Monte Carlo algorithms to obtain statistics of the unknowns. The likelihood calculation is performed by directly sampling the approximate stochastic solution obtained through the ASGC method. The technique is assessed on two nonlinear inverse problems: source inversion and permeability estimation in flow through porous media.
Inverse Problems of Low-Frequency Diagnostics of the Earth's Crust
NASA Astrophysics Data System (ADS)
Gaikovich, K. P.; Smirnov, A. I.
2015-11-01
We develop and test (in numerical simulation) methods of computer tomography of distributed under-surface inhomogeneities in the conductivity of a medium and for holography (i.e., reconstruction of the shape) of solid and uniformly composed subsurface objects in the context of the problems of multifrequency electromagnetic diagnostics of the Earth's crust structure in the extremely and ultra low frequency bands. The methods are based on solution of the inverse scattering problem using the results provided by multifrequency measurements of the distribution of complex amplitudes of the electromagnetic field on the surface of the medium under consideration.
Approximation methods for the solution of inverse problems in lake and sea sediment core analysis
NASA Technical Reports Server (NTRS)
Banks, H. T.; Rosen, I. G.
1985-01-01
A theoretical model employing one-dimensional (depth) transport equations to describe vertical redistribution of ocean-floor and lake-floor sediment (particulates, volcanic ash, microtektites, or radioactive tracers) by episodic and nonepisodic events including bioturbation is developed analytically and demonstrated. The principles underlying the model are explained; the model equations are derived; the inverse problem of identifying the depth-dependent bioturbation coefficient is addressed; two approximation theorems are presented; and numerical results for two sample problems are presented graphically. It is suggested that compatification, porosity effects, and depth-dependent sedimentation be taken into account when formulating future models.
A modification of the factorization method for the classical acoustic inverse scattering problems
NASA Astrophysics Data System (ADS)
Kirsch, Andreas; Liu, Xiaodong
2014-03-01
It is well-known that sampling type methods for solving inverse scattering problems fail if the wave number is an eigenvalue of a corresponding interior eigenvalue problem. By adding the far field patterns corresponding to an artificial ball lying within the obstacle and imposing an impedance boundary condition on the boundary of this ball we propose a modification of the factorization method which provides the characterization of the unknown obstacle for all wave numbers. Some numerical experiments are presented to demonstrate the feasibility and effectiveness of our method.
On parameterization of the inverse problem for estimating aquifer properties using tracer data
Kowalsky, M. B.; Finsterle, Stefan A.; Williams, Kenneth H.; Murray, Christopher J.; Commer, Michael; Newcomer, Darrell R.; Englert, Andreas L.; Steefel, Carl I.; Hubbard, Susan
2012-06-11
We consider a field-scale tracer experiment conducted in 2007 in a shallow uranium-contaminated aquifer at Rifle, Colorado. In developing a reliable approach for inferring hydrological properties at the site through inverse modeling of the tracer data, decisions made on how to parameterize heterogeneity (i.e., how to represent a heterogeneous distribution using a limited number of parameters that are amenable to estimation) are of paramount importance. We present an approach for hydrological inversion of the tracer data and explore, using a 2D synthetic example at first, how parameterization affects the solution, and how additional characterization data could be incorporated to reduce uncertainty. Specifically, we examine sensitivity of the results to the configuration of pilot points used in a geostatistical parameterization, and to the sampling frequency and measurement error of the concentration data. A reliable solution of the inverse problem is found when the pilot point configuration is carefully implemented. In addition, we examine the use of a zonation parameterization, in which the geometry of the geological facies is known (e.g., from geophysical data or core data), to reduce the non-uniqueness of the solution and the number of unknown parameters to be estimated. When zonation information is only available for a limited region, special treatment in the remainder of the model is necessary, such as using a geostatistical parameterization. Finally, inversion of the actual field data is performed using 2D and 3D models, and results are compared with slug test data.
NASA Astrophysics Data System (ADS)
Teschke, Gerd; Borries, Claudia
2010-02-01
This paper is concerned with the construction of an iterative algorithm to solve nonlinear inverse problems with an ell1 constraint on x. One extensively studied method to obtain a solution of such an ell1 penalized problem is iterative soft-thresholding. Regrettably, such iteration schemes are computationally very intensive. A subtle alternative to iterative soft-thresholding is the projected gradient method that was quite recently proposed by Daubechies et al (2008 J. Fourier Anal. Appl. 14 764-92). The authors have shown that the proposed scheme is indeed numerically much thriftier. However, its current applicability is limited to linear inverse problems. In this paper we provide an extension of this approach to nonlinear problems. Adequately adapting the conditions on the (variable) thresholding parameter to the nonlinear nature, we can prove convergence in norm for this projected gradient method, with and without acceleration. A numerical verification is given in the context of nonlinear and non-ideal sensing. For this particular recovery problem we can achieve an impressive numerical performance (when comparing it to non-accelerated procedures).
NASA Astrophysics Data System (ADS)
Mu, Xiyu; Cheng, Hao; Liu, Guoqing
2016-04-01
It is often difficult to provide the exact boundary condition in the practical use of variational method. The Euler equation derived from variational method cannot be solved without boundary condition. However, in some application problems such as the assimilation of remote sensing data, the values can be easily obtained in the inner region of the domain. Since the solution of elliptic partial differential equations continuously depends on the boundary condition, the boundary condition can be retrieved using part solutions in the inner area. In this paper, the variational problem of remote sensing data assimilation within a circular area is first established. The Klein-Gordon elliptic equation is derived from the Euler method of variational problems with assumed boundary condition. Secondly, a computer-friendly Green function is constructed for the Dirichlet problem of two-dimensional Klein-Gordon equation, with the formal solution according to Green formula. Thirdly, boundary values are retrieved by solving the optimal problem which is constructed according to the smoothness of boundary value function and the best approximation between formal solutions and high-accuracy measurements in the interior of the domain. Finally, the assimilation problem is solved on substituting the retrieved boundary values into the Klein-Gordon equation. It is a type of inverse problem in mathematics. The advantage of our method lies in that it needs no assumptions of the boundary condition. It thus alleviates the error introduced by artificial boundary condition in data fusion using variational method in the past.
Bayesian analysis of the neuromagnetic inverse problem with l(p)-norm priors.
Auranen, Toni; Nummenmaa, Aapo; Hämäläinen, Matti S; Jääskeläinen, Iiro P; Lampinen, Jouko; Vehtari, Aki; Sams, Mikko
2005-07-01
Magnetoencephalography (MEG) allows millisecond-scale non-invasive measurement of magnetic fields generated by neural currents in the brain. However, localization of the underlying current sources is ambiguous due to the so-called inverse problem. The most widely used source localization methods (i.e., minimum-norm and minimum-current estimates (MNE and MCE) and equivalent current dipole (ECD) fitting) require ad hoc determination of the cortical current distribution (l(2)-, l(1)-norm priors and point-sized dipolar, respectively). In this article, we perform a Bayesian analysis of the MEG inverse problem with l(p)-norm priors for the current sources. This way, we circumvent the arbitrary choice between l(1)- and l(2)-norm prior, which is instead rendered automatically based on the data. By obtaining numerical samples from the joint posterior probability distribution of the source current parameters and model hyperparameters (such as the l(p)-norm order p) using Markov chain Monte Carlo (MCMC) methods, we calculated the spatial inverse estimates as expectation values of the source current parameters integrated over the hyperparameters. Real MEG data and simulated (known) source currents with realistic MRI-based cortical geometry and 306-channel MEG sensor array were used. While the proposed model is sensitive to source space discretization size and computationally rather heavy, it is mathematically straightforward, thus allowing incorporation of, for instance, a priori functional magnetic resonance imaging (fMRI) information. PMID:15955497
Model-based elastography: a survey of approaches to the inverse elasticity problem
Doyley, M M
2012-01-01
Elastography is emerging as an imaging modality that can distinguish normal versus diseased tissues via their biomechanical properties. This article reviews current approaches to elastography in three areas — quasi-static, harmonic, and transient — and describes inversion schemes for each elastographic imaging approach. Approaches include: first-order approximation methods; direct and iterative inversion schemes for linear elastic; isotropic materials; and advanced reconstruction methods for recovering parameters that characterize complex mechanical behavior. The paper’s objective is to document efforts to develop elastography within the framework of solving an inverse problem, so that elastography may provide reliable estimates of shear modulus and other mechanical parameters. We discuss issues that must be addressed if model-based elastography is to become the prevailing approach to quasi-static, harmonic, and transient elastography: (1) developing practical techniques to transform the ill-posed problem with a well-posed one; (2) devising better forward models to capture the transient behavior of soft tissue; and (3) developing better test procedures to evaluate the performance of modulus elastograms. PMID:22222839
On the Optimization of the Inverse Problem for Bouguer Gravity Anomalies
NASA Astrophysics Data System (ADS)
Zamora, A.; Velasco, A. A.; Gutierrez, A. E.
2013-12-01
Inverse modeling of gravity data presents a very ill-posed mathematical problem, given that solutions are non-unique and small changes in parameters (position and density contrast of an anomalous body) can highly impact the resulting Earth's model. Although implementing 2- and 3-Dimensional gravitational inverse problems can determine the structural composition of the Earth, traditional inverse modeling approaches can be very unstable. A model of the shallow substructure is based on the density contrasts of anomalous bodies -with different densities with respect to a uniform region- or the boundaries between layers in a layered environment. We implement an interior-point method constrained optimization technique to improve the 2-D model of the Earth's structure through the use of known density constraints for transitional areas obtained from previous geological observations (e.g. core samples, seismic surveys, etc.). The proposed technique is applied to both synthetic data and gravitational data previously obtained from the Rio Grande Rift and the Cooper Flat Mine region located in Sierra County, New Mexico. We find improvements on the models obtained from this optimization scheme given that getting rid of geologically unacceptable models that would otherwise meet the required geophysical properties reduces the solution space.
NASA Astrophysics Data System (ADS)
Tenzer, Robert; Bargherbandi, Mohammad; Sjoeberg, Lars E.; Novák, Pavel
2013-04-01
The isostatic gravity anomalies have been traditionally used to solve the inverse problems of isostasy. Since gravity measurements are nowadays carried out together with GPS positioning, the utilization of gravity disturbances in various regional gravimetric applications becomes possible. In global studies, the gravity disturbances can be computed using global geopotential models which are currently available to a very high accuracy and resolution. In this study we facilitate the definition of the isostatic gravity disturbances in the Vening-Meinesz Moritz inverse problem of isostasy for finding the Moho depths. We further utilize uniform mathematical formalism in the gravimetric forward modelling based on methods for a spherical harmonic analysis and synthesis of gravity field. We then apply both mathematical procedures to determine globally the Moho depths using the isostatic gravity disturbances. The results of gravimetric inversion are finally compared with the global crustal seismic model CRUST2.0; the RMS fit of the gravimetric Moho model with CRUST2.0 is 5.3 km. This is considerably better than the RMS fit of 7.0 km obtained after using the isostatic gravity anomalies.
Solving the Linear Balance Equation on the Globe as a Generalized Inverse Problem
NASA Technical Reports Server (NTRS)
Lu, Huei-Iin; Robertson, Franklin R.
1999-01-01
A generalized (pseudo) inverse technique was developed to facilitate a better understanding of the numerical effects of tropical singularities inherent in the spectral linear balance equation (LBE). Depending upon the truncation, various levels of determinancy are manifest. The traditional fully-determined (FD) systems give rise to a strong response, while the under-determined (UD) systems yield a weak response to the tropical singularities. The over-determined (OD) systems result in a modest response and a large residual in the tropics. The FD and OD systems can be alternatively solved by the iterative method. Differences in the solutions of an UD system exist between the inverse technique and the iterative method owing to the non- uniqueness of the problem. A realistic balanced wind was obtained by solving the principal components of the spectral LBE in terms of vorticity in an intermediate resolution. Improved solutions were achieved by including the singular-component solutions which best fit the observed wind data.
A new reconstruction method for the inverse source problem from partial boundary measurements
NASA Astrophysics Data System (ADS)
Canelas, Alfredo; Laurain, Antoine; Novotny, Antonio A.
2015-07-01
The inverse source problem consists of reconstructing a mass distribution in a geometrical domain from boundary measurements of the associated potential and its normal derivative. In this paper the inverse source problem is reformulated as a topology optimization problem, where the support of the mass distribution is the unknown variable. The Kohn-Vogelius functional is minimized. It measures the misfit between the solutions of two auxiliary problems containing information about the boundary measurements. The Newtonian potential is used to complement the unavailable information on the hidden boundary. The resulting topology optimization algorithm is based on an analytic formula for the variation of the Kohn-Vogelius functional with respect to a class of mass distributions consisting of a finite number of ball-shaped trial anomalies. The proposed reconstruction algorithm is non-iterative and very robust with respect to noisy data. Finally, in order to show the effectiveness of the devised reconstruction algorithm, some numerical experiments in two and three spatial dimensions are presented.
Extraction of semiconductor dopant profiles from spreading resistance data: An inverse problem
NASA Astrophysics Data System (ADS)
Choo, S. C.; Leong, M. S.; Liem, Chandra B. T.; Kong, K. C.
1990-06-01
The traditional method of solving, on a layer-by-layer basis, the inverse problem of extracting resistivity values from spreading resistance measurements is found to produce wildly oscillatory, physically unacceptable resistivity profiles in the case of p-type silicon structures, where a resistivity-dependent probe contact radius is used in conjunction with the probe calibration data. These oscillations are manifestations of the fact that the inverse problem has non-unique solutions; they occur because the problem is inherently ill-posed. The well-known Tikhonov regularisation technique, which converts the present set of highly non-linear integral equations to an equivalent variational problem, is applied to stabilise the solution. Tests are performed on a variety of simulated profiles, and they reveal the existence of an optimum value for the regularisation parameter that is to be used with a second difference expression for the stabiliser of the cost function. When applied to measured spreading resistance data, the technique is found to produce results of reconstruction that are stable and physically reasonable.
Electrostatic point charge fitting as an inverse problem: Revealing the underlying ill-conditioning
NASA Astrophysics Data System (ADS)
Ivanov, Maxim V.; Talipov, Marat R.; Timerghazin, Qadir K.
2015-10-01
Atom-centered point charge (PC) model of the molecular electrostatics—a major workhorse of the atomistic biomolecular simulations—is usually parameterized by least-squares (LS) fitting of the point charge values to a reference electrostatic potential, a procedure that suffers from numerical instabilities due to the ill-conditioned nature of the LS problem. To reveal the origins of this ill-conditioning, we start with a general treatment of the point charge fitting problem as an inverse problem and construct an analytical model with the point charges spherically arranged according to Lebedev quadrature which is naturally suited for the inverse electrostatic problem. This analytical model is contrasted to the atom-centered point-charge model that can be viewed as an irregular quadrature poorly suited for the problem. This analysis shows that the numerical problems of the point charge fitting are due to the decay of the curvatures corresponding to the eigenvectors of LS sum Hessian matrix. In part, this ill-conditioning is intrinsic to the problem and is related to decreasing electrostatic contribution of the higher multipole moments, that are, in the case of Lebedev grid model, directly associated with the Hessian eigenvectors. For the atom-centered model, this association breaks down beyond the first few eigenvectors related to the high-curvature monopole and dipole terms; this leads to even wider spread-out of the Hessian curvature values. Using these insights, it is possible to alleviate the ill-conditioning of the LS point-charge fitting without introducing external restraints and/or constraints. Also, as the analytical Lebedev grid PC model proposed here can reproduce multipole moments up to a given rank, it may provide a promising alternative to including explicit multipole terms in a force field.
Solutions to the Inverse LQR Problem with Application to Biological Systems Analysis
Priess, M Cody; Conway, Richard; Choi, Jongeun; Popovich, John M; Radcliffe, Clark
2015-01-01
In this paper, we present a set of techniques for finding a cost function to the time-invariant Linear Quadratic Regulator (LQR) problem in both continuous- and discrete-time cases. Our methodology is based on the solution to the inverse LQR problem, which can be stated as: does a given controller K describe the solution to a time-invariant LQR problem, and if so, what weights Q and R produce K as the optimal solution? Our motivation for investigating this problem is the analysis of motion goals in biological systems. We first describe an efficient Linear Matrix Inequality (LMI) method for determining a solution to the general case of this inverse LQR problem when both the weighting matrices Q and R are unknown. Our first LMI-based formulation provides a unique solution when it is feasible. Additionally, we propose a gradient-based, least-squares minimization method that can be applied to approximate a solution in cases when the LMIs are infeasible. This new method is very useful in practice since the estimated gain matrix K from the noisy experimental data could be perturbed by the estimation error, which may result in the infeasibility of the LMIs. We also provide an LMI minimization problem to find a good initial point for the minimization using the proposed gradient descent algorithm. We then provide a set of examples to illustrate how to apply our approaches to several different types of problems. An important result is the application of the technique to human subject posture control when seated on a moving robot. Results show that we can recover a cost function which may provide a useful insight on the human motor control goal. PMID:26640359
Electrostatic point charge fitting as an inverse problem: Revealing the underlying ill-conditioning
Ivanov, Maxim V.; Talipov, Marat R.; Timerghazin, Qadir K.
2015-10-07
Atom-centered point charge (PC) model of the molecular electrostatics—a major workhorse of the atomistic biomolecular simulations—is usually parameterized by least-squares (LS) fitting of the point charge values to a reference electrostatic potential, a procedure that suffers from numerical instabilities due to the ill-conditioned nature of the LS problem. To reveal the origins of this ill-conditioning, we start with a general treatment of the point charge fitting problem as an inverse problem and construct an analytical model with the point charges spherically arranged according to Lebedev quadrature which is naturally suited for the inverse electrostatic problem. This analytical model is contrasted to the atom-centered point-charge model that can be viewed as an irregular quadrature poorly suited for the problem. This analysis shows that the numerical problems of the point charge fitting are due to the decay of the curvatures corresponding to the eigenvectors of LS sum Hessian matrix. In part, this ill-conditioning is intrinsic to the problem and is related to decreasing electrostatic contribution of the higher multipole moments, that are, in the case of Lebedev grid model, directly associated with the Hessian eigenvectors. For the atom-centered model, this association breaks down beyond the first few eigenvectors related to the high-curvature monopole and dipole terms; this leads to even wider spread-out of the Hessian curvature values. Using these insights, it is possible to alleviate the ill-conditioning of the LS point-charge fitting without introducing external restraints and/or constraints. Also, as the analytical Lebedev grid PC model proposed here can reproduce multipole moments up to a given rank, it may provide a promising alternative to including explicit multipole terms in a force field.
Electrostatic point charge fitting as an inverse problem: Revealing the underlying ill-conditioning.
Ivanov, Maxim V; Talipov, Marat R; Timerghazin, Qadir K
2015-10-01
Atom-centered point charge (PC) model of the molecular electrostatics-a major workhorse of the atomistic biomolecular simulations-is usually parameterized by least-squares (LS) fitting of the point charge values to a reference electrostatic potential, a procedure that suffers from numerical instabilities due to the ill-conditioned nature of the LS problem. To reveal the origins of this ill-conditioning, we start with a general treatment of the point charge fitting problem as an inverse problem and construct an analytical model with the point charges spherically arranged according to Lebedev quadrature which is naturally suited for the inverse electrostatic problem. This analytical model is contrasted to the atom-centered point-charge model that can be viewed as an irregular quadrature poorly suited for the problem. This analysis shows that the numerical problems of the point charge fitting are due to the decay of the curvatures corresponding to the eigenvectors of LS sum Hessian matrix. In part, this ill-conditioning is intrinsic to the problem and is related to decreasing electrostatic contribution of the higher multipole moments, that are, in the case of Lebedev grid model, directly associated with the Hessian eigenvectors. For the atom-centered model, this association breaks down beyond the first few eigenvectors related to the high-curvature monopole and dipole terms; this leads to even wider spread-out of the Hessian curvature values. Using these insights, it is possible to alleviate the ill-conditioning of the LS point-charge fitting without introducing external restraints and/or constraints. Also, as the analytical Lebedev grid PC model proposed here can reproduce multipole moments up to a given rank, it may provide a promising alternative to including explicit multipole terms in a force field. PMID:26450287
FOREWORD: 2nd International Workshop on New Computational Methods for Inverse Problems (NCMIP 2012)
NASA Astrophysics Data System (ADS)
Blanc-Féraud, Laure; Joubert, Pierre-Yves
2012-09-01
Conference logo This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 2nd International Workshop on New Computational Methods for Inverse Problems, (NCMIP 2012). This workshop took place at Ecole Normale Supérieure de Cachan, in Cachan, France, on 15 May 2012, at the initiative of Institut Farman. The first edition of NCMIP also took place in Cachan, France, within the scope of the ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/). The NCMIP Workshop focused on recent advances in the resolution of inverse problems. Indeed inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finance. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, kernel methods, learning methods, convex optimization, free discontinuity problems, metamodels, proper orthogonal decomposition
FOREWORD: 3rd International Workshop on New Computational Methods for Inverse Problems (NCMIP 2013)
NASA Astrophysics Data System (ADS)
Blanc-Féraud, Laure; Joubert, Pierre-Yves
2013-10-01
Conference logo This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 3rd International Workshop on New Computational Methods for Inverse Problems, NCMIP 2013 (http://www.farman.ens-cachan.fr/NCMIP_2013.html). This workshop took place at Ecole Normale Supérieure de Cachan, in Cachan, France, on 22 May 2013, at the initiative of Institut Farman. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of the ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/), and secondly at the initiative of Institut Farman, in May 2012 (http://www.farman.ens-cachan.fr/NCMIP_2012.html). The NCMIP Workshop focused on recent advances in the resolution of inverse problems. Indeed inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational
The method of functional representations in the solution of inverse problems of gravimetry
NASA Astrophysics Data System (ADS)
Kobrunov, A. I.
2015-07-01
The paper describes the method for solving the inverse problems of gravimetry based on the functional representations, which follow from the variational principles in the uniform metrics with respect to density models of the geological medium. The functional representations are obtained for both the linear problem (which study local density distributions) and nonlinear problem (which study a system of structural models). The explicit formulas for calculating density models are derived for the particular cases based on the introduced functional representations of the obtained solutions. In the general case, the converging iterative processes providing the solution for both the density distribution models and structural models are constructed. The relationship is established between the functional representations implementing the variational principle in the uniform metric and linear integral representations corresponding to the optimization in the quadratic norm, on one hand, and the other known density models, on the other hand.
The Inverse Problem for Confined Aquifer Flow: Identification and Estimation With Extensions
NASA Astrophysics Data System (ADS)
Loaiciga, Hugo A.; MariñO, Miguel A.
1987-01-01
The contributions of this work are twofold. First, a methodology for estimating the elements of parameter matrices in the governing equation of flow in a confined aquifer is developed. The estimation techniques for the distributed-parameter inverse problem pertain to linear least squares and generalized least squares methods. The linear relationship among the known heads and unknown parameters of the flow equation provides the background for developing criteria for determining the identifiability status of unknown parameters. Under conditions of exact or overidentification it is possible to develop statistically consistent parameter estimators and their asymptotic distributions. The estimation techniques, namely, two-stage least squares and three stage least squares, are applied to a specific groundwater inverse problem and compared between themselves and with an ordinary least squares estimator. The three-stage estimator provides the closer approximation to the actual parameter values, but it also shows relatively large standard errors as compared to the ordinary and two-stage estimators. The estimation techniques provide the parameter matrices required to simulate the unsteady groundwater flow equation. Second, a nonlinear maximum likelihood estimation approach to the inverse problem is presented. The statistical properties of maximum likelihood estimators are derived, and a procedure to construct confidence intervals and do hypothesis testing is given. The relative merits of the linear and maximum likelihood estimators are analyzed. Other topics relevant to the identification and estimation methodologies, i.e., a continuous-time solution to the flow equation, coping with noise-corrupted head measurements, and extension of the developed theory to nonlinear cases are also discussed. A simulation study is used to evaluate the methods developed in this study.
SimPEG: An open-source framework for geophysical simulations and inverse problems
NASA Astrophysics Data System (ADS)
Cockett, R.; Kang, S.; Heagy, L.
2014-12-01
Geophysical surveys are powerful tools for obtaining information about the subsurface. Inverse modelling provides a mathematical framework for constructing a model of physical property distributions that are consistent with the data collected by these surveys. The geosciences are increasingly moving towards the integration of geological, geophysical, and hydrological information to better characterize the subsurface. This integration must span disciplines and is not only challenging scientifically, but the inconsistencies between conventions often makes implementations complicated, non-reproducible, or inefficient. We have developed an open source software package for Simulation and Parameter Estimation in Geophysics (SimPEG), which provides a generalized framework for solving geophysical forward and inverse problems. SimPEG is written entirely in Python with minimal dependencies in the hopes that it can be used both as a research tool and for education. SimPEG includes finite volume discretizations on structured and unstructured meshes, interfaces to standard numerical solver packages, convex optimization algorithms, model parameterizations, and tailored visualization routines. The framework is modular and object-oriented, which promotes real time experimentation and combination of geophysical problems and inversion methodologies. In this presentation, we will highlight a few geophysical examples, including direct-current resistivity and electromagnetics, and discuss some of the challenges and successes we encountered in developing a flexible and extensible framework. Throughout development of SimPEG we have focused on simplicity, usability, documentation, and extensive testing. By embracing a fully open source development paradigm, we hope to encourage reproducible research, cooperation, and communication to help tackle some of the inherently multidisciplinary problems that face integrated geophysical methods.
A Subspace Pursuit–based Iterative Greedy Hierarchical Solution to the Neuromagnetic Inverse Problem
Babadi, Behtash; Obregon-Henao, Gabriel; Lamus, Camilo; Hämäläinen, Matti S.; Brown, Emery N.; Purdon, Patrick L.
2013-01-01
Magnetoencephalography (MEG) is an important non-invasive method for studying activity within the human brain. Source localization methods can be used to estimate spatiotemporal activity from MEG measurements with high temporal resolution, but the spatial resolution of these estimates is poor due to the ill-posed nature of the MEG inverse problem. Recent developments in source localization methodology have emphasized temporal as well as spatial constraints to improve source localization accuracy, but these methods can be computationally intense. Solutions emphasizing spatial sparsity hold tremendous promise, since the underlying neurophysiological processes generating MEG signals are often sparse in nature, whether in the form of focal sources, or distributed sources representing large-scale functional networks. Recent developments in the theory of compressed sensing (CS) provide a rigorous framework to estimate signals with sparse structure. In particular, a class of CS algorithms referred to as greedy pursuit algorithms can provide both high recovery accuracy and low computational complexity. Greedy pursuit algorithms are difficult to apply directly to the MEG inverse problem because of the high-dimensional structure of the MEG source space and the high spatial correlation in MEG measurements. In this paper, we develop a novel greedy pursuit algorithm for sparse MEG source localization that overcomes these fundamental problems. This algorithm, which we refer to as the Subspace Pursuit-based Iterative Greedy Hierarchical (SPIGH) inverse solution, exhibits very low computational complexity while achieving very high localization accuracy. We evaluate the performance of the proposed algorithm using comprehensive simulations, as well as the analysis of human MEG data during spontaneous brain activity and somatosensory stimuli. These studies reveal substantial performance gains provided by the SPIGH algorithm in terms of computational complexity, localization accuracy
A Hybrid Optimization Method for Solving Bayesian Inverse Problems under Uncertainty
Zhang, Kai; Wang, Zengfei; Zhang, Liming; Yao, Jun; Yan, Xia
2015-01-01
In this paper, we investigate the application of a new method, the Finite Difference and Stochastic Gradient (Hybrid method), for history matching in reservoir models. History matching is one of the processes of solving an inverse problem by calibrating reservoir models to dynamic behaviour of the reservoir in which an objective function is formulated based on a Bayesian approach for optimization. The goal of history matching is to identify the minimum value of an objective function that expresses the misfit between the predicted and measured data of a reservoir. To address the optimization problem, we present a novel application using a combination of the stochastic gradient and finite difference methods for solving inverse problems. The optimization is constrained by a linear equation that contains the reservoir parameters. We reformulate the reservoir model’s parameters and dynamic data by operating the objective function, the approximate gradient of which can guarantee convergence. At each iteration step, we obtain the relatively ‘important’ elements of the gradient, which are subsequently substituted by the values from the Finite Difference method through comparing the magnitude of the components of the stochastic gradient, which forms a new gradient, and we subsequently iterate with the new gradient. Through the application of the Hybrid method, we efficiently and accurately optimize the objective function. We present a number numerical simulations in this paper that show that the method is accurate and computationally efficient. PMID:26252392
Bayesian approach to inverse problems for functions with a variable-index Besov prior
NASA Astrophysics Data System (ADS)
Jia, Junxiong; Peng, Jigen; Gao, Jinghuai
2016-08-01
The Bayesian approach has been adopted to solve inverse problems that reconstruct a function from noisy observations. Prior measures play a key role in the Bayesian method. Hence, many probability measures have been proposed, among which total variation (TV) is a well-known prior measure that can preserve sharp edges. However, it has two drawbacks, the staircasing effect and a lack of the discretization-invariant property. The variable-index TV prior has been proposed and analyzed in the area of image analysis for the former, and the Besov prior has been employed recently for the latter. To overcome both issues together, in this paper, we present a variable-index Besov prior measure, which is a non-Gaussian measure. Some useful properties of this new prior measure have been proven for functions defined on a torus. We have also generalized Bayesian inverse theory in infinite dimensions for our new setting. Finally, this theory has been applied to integer- and fractional-order backward diffusion problems. To the best of our knowledge, this is the first time that the Bayesian approach has been used for the fractional-order backward diffusion problem, which provides an opportunity to quantify its uncertainties.
A 2D forward and inverse code for streaming potential problems
NASA Astrophysics Data System (ADS)
Soueid Ahmed, A.; Jardani, A.; Revil, A.
2013-12-01
The self-potential method corresponds to the passive measurement of the electrical field in response to the occurrence of natural sources of current in the ground. One of these sources corresponds to the streaming current associated with the flow of the groundwater. We can therefore apply the self- potential method to recover non-intrusively some information regarding the groundwater flow. We first solve the forward problem starting with the solution of the groundwater flow problem, then computing the source current density, and finally solving a Poisson equation for the electrical potential. We use the finite-element method to solve the relevant partial differential equations. In order to reduce the number of (petrophysical) model parameters required to solve the forward problem, we introduced an effective charge density tensor of the pore water, which can be determined directly from the permeability tensor for neutral pore waters. The second aspect of our work concerns the inversion of the self-potential data using Tikhonov regularization with smoothness and weighting depth constraints. This approach accounts for the distribution of the electrical resistivity, which can be independently and approximately determined from electrical resistivity tomography. A numerical code, SP2DINV, has been implemented in Matlab to perform both the forward and inverse modeling. Three synthetic case studies are discussed.
Implementation of probabilistic approach in solving inverse problems as a grid-backed web service.
NASA Astrophysics Data System (ADS)
Kholodkov, K. I.; Aleshin, I. M.; Koryagin, V. N.; Shogin, A. N.; Sukhoroslov, O. V.
2012-04-01
In this work probabilistic approach to inverse problem was adopted. It leads to definition and sampling of a posteriori probability density function (APDF), which combines a priori system information with information, derived from observation data. Use of APDF implies significant computational resourses consumption, even for moderate model parameter count. However the computation of APDF value at different points is carried out completely independently, therefore this problem is considered ideal for loosely coupled distributed computing system. Globus Toolkit middleware was used, including the GridFTP for data transfer and GRAM for execution control, as well as TORQUE resource manager for each computing node. To reduce the hardware cost all grid services, except for GridFTP, run as virtual guests on execution nodes. Due to very insignificant resources utilization the guests make no footprint on node's computation power. To hide complex middleware interface from scientific users, user friendly web interface was created, which provides restricted but sufficient tool set. Determination of seismic anisotropy by wave form inversion was implemented as model problem. The interface allows user to edit model parameters, estimate execution time for specified parameter set, run calculation and perform result visualization. Details of start-up, management and results acquisition are hidden from user. This work was supported by Russian Foundation of Basic Research, grants 10-07-00491-a, 11-05-00988-a and 11-07-12045-ofi-m-2011
NASA Astrophysics Data System (ADS)
Mejer Hansen, Thomas; Skou Cordua, Knud; Caroline Looms, Majken; Mosegaard, Klaus
2013-03-01
From a probabilistic point-of-view, the solution to an inverse problem can be seen as a combination of independent states of information quantified by probability density functions. Typically, these states of information are provided by a set of observed data and some a priori information on the solution. The combined states of information (i.e. the solution to the inverse problem) is a probability density function typically referred to as the a posteriori probability density function. We present a generic toolbox for Matlab and Gnu Octave called SIPPI that implements a number of methods for solving such probabilistically formulated inverse problems by sampling the a posteriori probability density function. In order to describe the a priori probability density function, we consider both simple Gaussian models and more complex (and realistic) a priori models based on higher order statistics. These a priori models can be used with both linear and non-linear inverse problems. For linear inverse Gaussian problems we make use of least-squares and kriging-based methods to describe the a posteriori probability density function directly. For general non-linear (i.e. non-Gaussian) inverse problems, we make use of the extended Metropolis algorithm to sample the a posteriori probability density function. Together with the extended Metropolis algorithm, we use sequential Gibbs sampling that allow computationally efficient sampling of complex a priori models. The toolbox can be applied to any inverse problem as long as a way of solving the forward problem is provided. Here we demonstrate the methods and algorithms available in SIPPI. An application of SIPPI, to a tomographic cross borehole inverse problems, is presented in a second part of this paper.
NASA Technical Reports Server (NTRS)
Liu, Gao-Lian
1991-01-01
Advances in inverse design and optimization theory in engineering fields in China are presented. Two original approaches, the image-space approach and the variational approach, are discussed in terms of turbomachine aerodynamic inverse design. Other areas of research in turbomachine aerodynamic inverse design include the improved mean-streamline (stream surface) method and optimization theory based on optimal control. Among the additional engineering fields discussed are the following: the inverse problem of heat conduction, free-surface flow, variational cogeneration of optimal grid and flow field, and optimal meshing theory of gears.
A necessary condition for applying MUSIC algorithm in limited-view inverse scattering problem
NASA Astrophysics Data System (ADS)
Park, Taehoon; Park, Won-Kwang
2015-09-01
Throughout various results of numerical simulations, it is well-known that MUltiple SIgnal Classification (MUSIC) algorithm can be applied in the limited-view inverse scattering problems. However, the application is somehow heuristic. In this contribution, we identify a necessary condition of MUSIC for imaging of collection of small, perfectly conducting cracks. This is based on the fact that MUSIC imaging functional can be represented as an infinite series of Bessel function of integer order of the first kind. Numerical experiments from noisy synthetic data supports our investigation.
Lisin, E. A.; Lisina, I. I.; Vaulina, O. S.; Petrov, O. F.
2015-03-15
Solution of the inverse Langevin problem is presented for open dissipative systems with anisotropic interparticle interaction. Possibility of applying this solution for experimental determining the anisotropic interaction forces between dust particles in complex plasmas with ion flow is considered. For this purpose, we have tested the method on the results of numerical simulation of chain structures of particles with quasidipole-dipole interaction, similar to the one occurring due to effects of ion focusing in gas discharges. Influence of charge spatial inhomogeneity and fluctuations on the results of recovery is also discussed.
Simple method for inference in inverse Ising problem using full data
NASA Astrophysics Data System (ADS)
Kiwata, Hirohito
2015-10-01
We consider inference in the inverse Ising problem using full data, which means incorporating sets of spin configurations. We approximate the Boltzmann distribution of the system to generate a frequency distribution derived from the given data. Then, the ratio between two Boltzmann distributions with different spin configurations eliminates the partition function and we obtain linear equations which can be solved to yield statistical parameters. Our method is applicable to cases where the absolute values of the coupling parameters and external fields are large. Compared to pseudolikelihood maximization, the accuracy of the inference obtained from our method is similar, although our approach is less labor intensive.
Saint-Venant's problem and semi-inverse solutions in nonlinear elasticity
NASA Astrophysics Data System (ADS)
Mielke, Alexander
1988-09-01
Saint-Venant's problem consists in finding elastic deformations of an infinite prismatic body taking given values for the cross-sectional resultants of force and moment. Using the center manifold approach we show that all deformations having sufficiently small bounded strains lie on a finite-dimensional manifold. In particular, the flow on this manifold is described by a set of equations having exactly the form of the classical rod equations. Moreover, the set of semi-inverse solutions can be analyzed locally.
Chernichenko, Yu.D.
2005-01-01
Within the relativistic quasipotential approach to quantum field theory, the relativistic inverse scattering problem is solved for the case where the total quasipotential describing the interaction of two relativistic spinless particles having different masses is a superposition of a nonlocal separable and a local quasipotential. It is assumed that the local component of the total quasipotential is known and that there exist bound states in this local component. It is shown that the nonlocal separable component of the total interaction can be reconstructed provided that the local component, an increment of the phase shift, and the energies of bound states are known.
Poincaré inverse problem and torus construction in phase space
NASA Astrophysics Data System (ADS)
Laakso, Teemu; Kaasalainen, Mikko
2016-02-01
The phase space of an integrable Hamiltonian system is foliated by invariant tori. For an arbitrary Hamiltonian H such a foliation may not exist, but we can artificially construct one through a parameterised family of surfaces, with the intention of finding, in some sense, the closest integrable approximation to H. This is the Poincaré inverse problem (PIP). In this paper, we review the available methods of solving the PIP and present a new iterative approach which works well for the often problematic thin orbits.
Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems
Lu, Fei; Morzfeld, Matthias; Tu, Xuemin; Chorin, Alexandre J.
2015-02-01
Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpensively. We show, by analysis and example, that when the data contain significant information beyond what is assumed in the prior, the surrogate posterior can be very different from the posterior, and the resulting estimates become inaccurate. One can improve the accuracy by adaptively increasing the order of the polynomial chaos, but the cost may increase too fast for this to be cost effective compared to Monte Carlo sampling without a surrogate posterior.
A nodal inverse problem for a quasi-linear ordinary differential equation in the half-line
NASA Astrophysics Data System (ADS)
Pinasco, Juan P.; Scarola, Cristian
2016-07-01
In this paper we study an inverse problem for a quasi-linear ordinary differential equation with a monotonic weight in the half-line. First, we find the asymptotic behavior of the singular eigenvalues, and we obtain a Weyl-type asymptotics imposing an appropriate integrability condition on the weight. Then, we investigate the inverse problem of recovering the coefficients from nodal data. We show that any dense subset of nodes of the eigenfunctions is enough to recover the weight.
A theoretical formulation of the electrophysiological inverse problem on the sphere
NASA Astrophysics Data System (ADS)
Riera, Jorge J.; Valdés, Pedro A.; Tanabe, Kunio; Kawashima, Ryuta
2006-04-01
The construction of three-dimensional images of the primary current density (PCD) produced by neuronal activity is a problem of great current interest in the neuroimaging community, though being initially formulated in the 1970s. There exist even now enthusiastic debates about the authenticity of most of the inverse solutions proposed in the literature, in which low resolution electrical tomography (LORETA) is a focus of attention. However, in our opinion, the capabilities and limitations of the electro and magneto encephalographic techniques to determine PCD configurations have not been extensively explored from a theoretical framework, even for simple volume conductor models of the head. In this paper, the electrophysiological inverse problem for the spherical head model is cast in terms of reproducing kernel Hilbert spaces (RKHS) formalism, which allows us to identify the null spaces of the implicated linear integral operators and also to define their representers. The PCD are described in terms of a continuous basis for the RKHS, which explicitly separates the harmonic and non-harmonic components. The RKHS concept permits us to bring LORETA into the scope of the general smoothing splines theory. A particular way of calculating the general smoothing splines is illustrated, avoiding a brute force discretization prematurely. The Bayes information criterion is used to handle dissimilarities in the signal/noise ratios and physical dimensions of the measurement modalities, which could affect the estimation of the amount of smoothness required for that class of inverse solution to be well specified. In order to validate the proposed method, we have estimated the 3D spherical smoothing splines from two data sets: electric potentials obtained from a skull phantom and magnetic fields recorded from subjects performing an experiment of human faces recognition.
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Fowler, Michael James
2014-04-25
In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy
Mature red blood cells: from optical model to inverse light-scattering problem
Gilev, Konstantin V.; Yurkin, Maxim A.; Chernyshova, Ekaterina S.; Strokotov, Dmitry I.; Chernyshev, Andrei V.; Maltsev, Valeri P.
2016-01-01
We propose a method for characterization of mature red blood cells (RBCs) morphology, based on measurement of light-scattering patterns (LSPs) of individual RBCs with the scanning flow cytometer and on solution of the inverse light-scattering (ILS) problem for each LSP. We considered a RBC shape model, corresponding to the minimal bending energy of the membrane with isotropic elasticity, and constructed an analytical approximation, which allows rapid simulation of the shape, given the diameter and minimal and maximal thicknesses. The ILS problem was solved by the nearest-neighbor interpolation using a preliminary calculated database of 250,000 theoretical LSPs. For each RBC in blood sample we determined three abovementioned shape characteristics and refractive index, which also allows us to calculate volume, surface area, sphericity index, spontaneous curvature, hemoglobin concentration and content. PMID:27446656
NASA Technical Reports Server (NTRS)
Murio, Diego A.
1991-01-01
An explicit and unconditionally stable finite difference method for the solution of the transient inverse heat conduction problem in a semi-infinite or finite slab mediums subject to nonlinear radiation boundary conditions is presented. After measuring two interior temperature histories, the mollification method is used to determine the surface transient heat source if the energy radiation law is known. Alternatively, if the active surface is heated by a source at a rate proportional to a given function, the nonlinear surface radiation law is then recovered as a function of the interface temperature when the problem is feasible. Two typical examples corresponding to Newton cooling law and Stefan-Boltzmann radiation law respectively are illustrated. In all cases, the method predicts the surface conditions with an accuracy suitable for many practical purposes.
NASA Astrophysics Data System (ADS)
Fredman, T. P.
2004-12-01
A boundary identification problem in inverse heat conduction is studied, based on data from internal measurement of temperature and heat flux. Formulated as a sideways heat conduction equation, a spatial continuation technique is applied to extend the solution to a known boundary condition at the desired boundary position. Recording the positions traversed in the continuation for each time instant yields the boundary position trajectory and hence the solution of the identification problem. A prospective application of the method can be found in the ironmaking blast furnace, where it is desired to monitor the thickness of the accreted refractory wall based on measurement of its internal state. Simulations featuring noisy measurement data demonstrate the feasibility of the identification method for blast furnace wall thickness estimation.
NASA Astrophysics Data System (ADS)
Egger, Herbert; Engl, Heinz W.
2005-06-01
This paper investigates the stable identification of local volatility surfaces σ(S, t) in the Black-Scholes/Dupire equation from market prices of European Vanilla options. Based on the properties of the parameter-to-solution mapping, which assigns option prices to given volatilities, we show stability and convergence of approximations gained by Tikhonov regularization. In the case of a known term-structure of the volatility surface, in particular, if the volatility is assumed to be constant in time, we prove convergence rates under simple smoothness and decay conditions on the true volatility. The convergence rate analysis sheds light onto the importance of an appropriate a priori guess for the unknown volatility and the nature of the ill-posedness of the inverse problem, caused by smoothing properties and the nonlinearity of the direct problem. Finally, the theoretical results are illustrated by numerical experiments.
SEMI-DEFINITE PROGRAMMING TECHNIQUES FOR STRUCTURED QUADRATIC INVERSE EIGENVALUE PROBLEMS
LIN, MATTHEW M.; DONG, BO; CHU, MOODY T.
2014-01-01
In the past decade or so, semi-definite programming (SDP) has emerged as a powerful tool capable of handling a remarkably wide range of problems. This article describes an innovative application of SDP techniques to quadratic inverse eigenvalue problems (QIEPs). The notion of QIEPs is of fundamental importance because its ultimate goal of constructing or updating a vibration system from some observed or desirable dynamical behaviors while respecting some inherent feasibility constraints well suits many engineering applications. Thus far, however, QIEPs have remained challenging both theoretically and computationally due to the great variations of structural constraints that must be addressed. Of notable interest and significance are the uniformity and the simplicity in the SDP formulation that solves effectively many otherwise very difficult QIEPs. PMID:25392603
A hybrid differential evolution/Levenberg-Marquardt method for solving inverse transport problems
Bledsoe, Keith C; Favorite, Jeffrey A
2010-01-01
Recently, the Differential Evolution (DE) optimization method was applied to solve inverse transport problems in finite cylindrical geometries and was shown to be far superior to the Levenberg-Marquardt optimization method at finding a global optimum for problems with several unknowns. However, while extremely adept at finding a global optimum solution, the DE method often requires a large number (hundreds or thousands) of transport calculations, making it much slower than the Levenberg-Marquardt method. In this paper, a hybridization of the Differential Evolution and Levenberg-Marquardt approaches is presented. This hybrid method takes advantage of the robust search capability of the Differential Evolution method and the speed of the Levenberg-Marquardt technique.
A hybrid algorithm for solving the EEG inverse problem from spatio-temporal EEG data.
Crevecoeur, Guillaume; Hallez, Hans; Van Hese, Peter; D'Asseler, Yves; Dupré, Luc; Van de Walle, Rik
2008-08-01
Epilepsy is a neurological disorder caused by intense electrical activity in the brain. The electrical activity, which can be modelled through the superposition of several electrical dipoles, can be determined in a non-invasive way by analysing the electro-encephalogram. This source localization requires the solution of an inverse problem. Locally convergent optimization algorithms may be trapped in local solutions and when using global optimization techniques, the computational effort can become expensive. Fast recovery of the electrical sources becomes difficult that way. Therefore, there is a need to solve the inverse problem in an accurate and fast way. This paper performs the localization of multiple dipoles using a global-local hybrid algorithm. Global convergence is guaranteed by using space mapping techniques and independent component analysis in a computationally efficient way. The accuracy is locally obtained by using the Recursively Applied and Projected-MUltiple Signal Classification (RAP-MUSIC) algorithm. When using this hybrid algorithm, a four times faster solution is obtained. PMID:18427852
NASA Astrophysics Data System (ADS)
Al-Ma'shumah, Fathimah; Permana, Dony; Sidarto, Kuntjoro Adji
2015-12-01
Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention.
Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian Inverse Problems
NASA Astrophysics Data System (ADS)
Lan, Shiwei; Bui-Thanh, Tan; Christie, Mike; Girolami, Mark
2016-03-01
The Bayesian approach to Inverse Problems relies predominantly on Markov Chain Monte Carlo methods for posterior inference. The typical nonlinear concentration of posterior measure observed in many such Inverse Problems presents severe challenges to existing simulation based inference methods. Motivated by these challenges the exploitation of local geometric information in the form of covariant gradients, metric tensors, Levi-Civita connections, and local geodesic flows have been introduced to more effectively locally explore the configuration space of the posterior measure. However, obtaining such geometric quantities usually requires extensive computational effort and despite their effectiveness affects the applicability of these geometrically-based Monte Carlo methods. In this paper we explore one way to address this issue by the construction of an emulator of the model from which all geometric objects can be obtained in a much more computationally feasible manner. The main concept is to approximate the geometric quantities using a Gaussian Process emulator which is conditioned on a carefully chosen design set of configuration points, which also determines the quality of the emulator. To this end we propose the use of statistical experiment design methods to refine a potentially arbitrarily initialized design online without destroying the convergence of the resulting Markov chain to the desired invariant measure. The practical examples considered in this paper provide a demonstration of the significant improvement possible in terms of computational loading suggesting this is a promising avenue of further development.
NASA Astrophysics Data System (ADS)
Lawrence, Chris C.; Febbraro, Michael; Flaska, Marek; Pozzi, Sara A.; Becchetti, F. D.
2016-08-01
Verification of future warhead-dismantlement treaties will require detection of certain warhead attributes without the disclosure of sensitive design information, and this presents an unusual measurement challenge. Neutron spectroscopy—commonly eschewed as an ill-posed inverse problem—may hold special advantages for warhead verification by virtue of its insensitivity to certain neutron-source parameters like plutonium isotopics. In this article, we investigate the usefulness of unfolded neutron spectra obtained from organic-scintillator data for verifying a particular treaty-relevant warhead attribute: the presence of high-explosive and neutron-reflecting materials. Toward this end, several improvements on current unfolding capabilities are demonstrated: deuterated detectors are shown to have superior response-matrix condition to that of standard hydrogen-base scintintillators; a novel data-discretization scheme is proposed which removes important detector nonlinearities; and a technique is described for re-parameterizing the unfolding problem in order to constrain the parameter space of solutions sought, sidestepping the inverse problem altogether. These improvements are demonstrated with trial measurements and verified using accelerator-based time-of-flight calculation of reference spectra. Then, a demonstration is presented in which the elemental compositions of low-Z neutron-attenuating materials are estimated to within 10%. These techniques could have direct application in verifying the presence of high-explosive materials in a neutron-emitting test item, as well as other for treaty verification challenges.
NASA Technical Reports Server (NTRS)
Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.
2010-01-01
The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.
LINPRO: Linear inverse problem library for data contaminated by statistical noise
NASA Astrophysics Data System (ADS)
Magierski, Piotr; Wlazłowski, Gabriel
2012-10-01
The library LINPRO which provides the solution to the linear inverse problem for data contaminated by a statistical noise is presented. The library makes use of two methods: Maximum Entropy Method and Singular Value Decomposition. As an example it has been applied to perform an analytic continuation of the imaginary time propagator obtained within the Quantum Monte Carlo method. Program summary Program title: LINPRO v1.0. Catalogue identifier: AEMT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEMT_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland. Licensing provisions: GNU Lesser General Public Licence. No. of lines in distributed program, including test data, etc.: 110620. No. of bytes in distributed program, including test data, etc.: 3208593. Distribution format: tar.gz. Programming language: C++. Computer: LINPRO library should compile on any computing system that has C++ compiler. Operating system: Linux or Unix. Classification: 4.9, 4.12, 4.13. External routines: OPT++: An Object-Oriented Nonlinear Optimization Library [1] (included in the distribution). Nature of problem: LINPRO library solves linear inverse problems with an arbitrary kernel and arbitrary external constraints imposed on the solution. Solution method: LINPRO library implements two complementary methods: Maximum Entropy Method and SVD method. Additional comments: Tested with compilers-GNU Compiler g++, Intel Compiler icpc. Running time: Problem dependent, ranging from seconds to hours. Each of the examples takes less than a minute to run. References: [1] OPT++: An Object-Oriented Nonlinear Optimization Library, https://software.sandia.gov/opt++/.
Baker, J.R.; Budinger, T.F.; Huesman, R.H.
1992-10-01
A major limitation in tomographic inverse problems is inadequate computation speed, which frequently impedes the application of engineering ideas and principles in medical science more than in the physical and engineering sciences. Medical problems are computationally taxing because a minimum description of the system often involves 5 dimensions (3 space, 1 energy, 1 time), with the range of each space coordinate requiring up to 512 samples. The computational tasks for this problem can be simply expressed by posing the problem as one in which the tomograph system response function is spatially invariant, and the noise is additive and Gaussian. Under these assumptions, a number of reconstruction methods have been implemented with generally satisfactory results for general medical imaging purposes. However, if the system response function of the tomograph is assumed more realistically to be spatially variant and the noise to be Poisson, the computational problem becomes much more difficult. Some of the algorithms being studied to compensate for position dependent resolution and statistical fluctuations in the data acquisition process, when expressed in canonical form, are not practical for clinical applications because the number of computations necessary exceeds the capabilities of high performance computer systems currently available. Reconstruction methods based on natural pixels, specifically orthonormal natural pixels, preserve symmetries in the data acquisition process. Fast implementations of orthonormal natural pixel algorithms can achieve orders of magnitude speedup relative to general implementations. Thus, specialized thought in algorithm development can lead to more significant increases in performance than can be achieved through hardware improvements alone.
NASA Astrophysics Data System (ADS)
Chyuan, Shiang-Woei; Liao, Yunn-Shiuan; Chen, Jeng-Tzong
2004-09-01
Engineers usually adopt multilayered design for semiconductor and electron devices, and an accurate electrostatic analysis is indispensable in the design stage. For variable design of electron devices, the BEM has become a better method than the domain-type FEM because BEM can provide a complete solution in terms of boundary values only, with substantial saving in modelling effort. Since dual BEM still has some advantages over conventional BEM for singularity arising from a degenerate boundary, the dual BEM accompanied by subregion technology, instead of tedious calculation of Fourier-Bessel transforms for the spatial Green's functions, was used to efficiently simulate the electric effect of diverse ratios of permittivity between arbitrarily multilayered domain and the fringing field around the edge of conductors. Results show that different ratios of permittivity will affect the electric field seriously, and the values of surface charge density on the edge of conductors are much higher than those on the middle part because of fringing effect. In addition, if using the DBEM to model the fringing field around the edge of conductors, the minimum allowable data of dielectric strength for keeping off dielectric breakdown can be obtained very efficiently.
NASA Astrophysics Data System (ADS)
Bui-Thanh, T.; Girolami, M.
2014-11-01
We consider the Riemann manifold Hamiltonian Monte Carlo (RMHMC) method for solving statistical inverse problems governed by partial differential equations (PDEs). The Bayesian framework is employed to cast the inverse problem into the task of statistical inference whose solution is the posterior distribution in infinite dimensional parameter space conditional upon observation data and Gaussian prior measure. We discretize both the likelihood and the prior using the H1-conforming finite element method together with a matrix transfer technique. The power of the RMHMC method is that it exploits the geometric structure induced by the PDE constraints of the underlying inverse problem. Consequently, each RMHMC posterior sample is almost uncorrelated/independent from the others providing statistically efficient Markov chain simulation. However this statistical efficiency comes at a computational cost. This motivates us to consider computationally more efficient strategies for RMHMC. At the heart of our construction is the fact that for Gaussian error structures the Fisher information matrix coincides with the Gauss-Newton Hessian. We exploit this fact in considering a computationally simplified RMHMC method combining state-of-the-art adjoint techniques and the superiority of the RMHMC method. Specifically, we first form the Gauss-Newton Hessian at the maximum a posteriori point and then use it as a fixed constant metric tensor throughout RMHMC simulation. This eliminates the need for the computationally costly differential geometric Christoffel symbols, which in turn greatly reduces computational effort at a corresponding loss of sampling efficiency. We further reduce the cost of forming the Fisher information matrix by using a low rank approximation via a randomized singular value decomposition technique. This is efficient since a small number of Hessian-vector products are required. The Hessian-vector product in turn requires only two extra PDE solves using the adjoint
NASA Astrophysics Data System (ADS)
Sanan, P.; Schnepp, S. M.; May, D.; Schenk, O.
2014-12-01
Geophysical applications require efficient forward models for non-linear Stokes flow on high resolution spatio-temporal domains. The bottleneck in applying the forward model is solving the linearized, discretized Stokes problem which takes the form of a large, indefinite (saddle point) linear system. Due to the heterogeniety of the effective viscosity in the elliptic operator, devising effective preconditioners for saddle point problems has proven challenging and highly problem-dependent. Nevertheless, at least three approaches show promise for preconditioning these difficult systems in an algorithmically scalable way using multigrid and/or domain decomposition techniques. The first is to work with a hierarchy of coarser or smaller saddle point problems. The second is to use the Schur complement method to decouple and sequentially solve for the pressure and velocity. The third is to use the Schur decomposition to devise preconditioners for the full operator. These involve sub-solves resembling inexact versions of the sequential solve. The choice of approach and sub-methods depends crucially on the motivating physics, the discretization, and available computational resources. Here we examine the performance trade-offs for preconditioning strategies applied to idealized models of mantle convection and lithospheric dynamics, characterized by large viscosity gradients. Due to the arbitrary topological structure of the viscosity field in geodynamical simulations, we utilize low order, inf-sup stable mixed finite element spatial discretizations which are suitable when sharp viscosity variations occur in element interiors. Particular attention is paid to possibilities within the decoupled and approximate Schur complement factorization-based monolithic approaches to leverage recently-developed flexible, communication-avoiding, and communication-hiding Krylov subspace methods in combination with `heavy' smoothers, which require solutions of large per-node sub-problems, well
NASA Astrophysics Data System (ADS)
Rudenko, O. V.; Gurbatov, S. N.
2016-07-01
Inverse problems of nonlinear acoustics have important applied significance. On the one hand, they are necessary for nonlinear diagnostics of media, materials, manufactured articles, building units, and biological and geological structures. On the other hand, they are needed for creating devices that ensure optimal action of acoustic radiation on a target. However, despite the many promising applications, this direction remains underdeveloped, especially for strongly distorted high-intensity waves containing shock fronts. An example of such an inverse problem is synthesis of the spatiotemporal structure of a field in a radiating system that ensures the highest possible energy density in the focal region. This problem is also related to the urgent problems of localizing wave energy and the theory of strongly nonlinear waves. Below we analyze some quite general and simple inverse nonlinear problems.
Inverse problem for a one-dimensional dynamical Dirac system (BC-method)
NASA Astrophysics Data System (ADS)
Belishev, M. I.; Mikhailov, V. S.
2014-12-01
A forward problem for the Dirac system is to find u=≤ft( \\begin{array}{ccccccccccccccc} {{u}1}(x,t) \\\\ {{u}2}(x,t) \\\\ \\end{array} \\right) obeying i{{u}t}+≤ft( \\begin{array}{ccccccccccccccc} 0 & 1 \\\\ -1 & 0 \\\\ \\end{array} \\right){{u}x}+≤ft( \\begin{array}{ccccccccccccccc} p & q \\\\ q & -p \\\\ \\end{array} \\right)u=0 for x\\gt 0, t\\gt 0; u(x,0)=≤ft( \\begin{array}{ccccccccccccccc} 0 \\\\ 0 \\\\ \\end{array} \\right) for x≥slant 0, and {{u}1}(0,t)=f(t) for t\\gt 0, with the real p=p(x),q=q(x). An input-output map R:{{u}1}(0,\\cdot )\\mapsto {{u}2}(0,\\cdot ) is of the convolution form Rf=if+r*f, where r=r(t) is a response function. By hyperbolicity of the system, for any T\\gt 0, function r{{|}0≤slant t≤slant 2T} is determined by p,q{{|}0≤slant x≤slant T}. An inverse problem is the following: for an (arbitrary) fixed T\\gt 0, given r{{|}0≤slant t≤slant 2T}, to recover p,q{{|}0≤slant x≤slant T}. The procedure that determines p,q is proposed, and the characteristic solvability conditions on r are provided. Our approach is purely time domain and is based on studying the controllability properties of the Dirac system. In itself the system is not controllable: the local completeness of states does not hold, but its relevant extension gains controllability. This is the fact that enables one to apply the boundary control method for solving the inverse problem.
Identification of dynamic characteristics of flexible rotors as dynamic inverse problem
NASA Technical Reports Server (NTRS)
Roisman, W. P.; Vajingortin, L. D.
1991-01-01
The problem of dynamic and balancing of flexible rotors were considered, which were set and solved as the problem of the identification of flexible rotor systems, which is the same as the inverse problem of the oscillation theory dealing with the task of the identifying the outside influences and system parameters on the basis of the known laws of motion. This approach to the problem allows the disclosure the picture of disbalances throughout the rotor-under-test (which traditional methods of flexible rotor balancing, based on natural oscillations, could not provide), and identify dynamic characteristics of the system, which correspond to a selected mathematical model. Eventually, various methods of balancing were developed depending on the special features of the machines as to their design, technology, and operation specifications. Also, theoretical and practical methods are given for the flexible rotor balancing at far from critical rotation frequencies, which does not necessarily require the knowledge forms of oscillation, dissipation, and elasticity and inertia characteristics, and to use testing masses.
Rosetta Consert Radio Sounding Experiment: A Numerical Method for the Inverse Problem
NASA Astrophysics Data System (ADS)
Cardiet, M.; Herique, A.; Rogez, Y.; Douté, S.; Kofman, W. W.
2014-12-01
Rosetta's module Philae will soon land on 67P CG nucleus, giving unprecedented insight about a comet nucleus, its composition and interior. The CONSERT instrument is one of the 20 scientific instruments of the mission. It's a bistatic two-modules radar, one on the orbiter, one on the lander. They generate EM waves that are transmitted through the nucleus. The signal is therefore delayed and attenuated by the nucleus materials and possible inhomogeneities. An accurate measurement and processing of these signals, repeated along the orbit, will allow us to perform a tomography, and for the first time, map the dielectric properties of a comet nucleus internal structures .Our approach for the resolution of this inverse problem is to use a custom built software called SIMSERT, which simulates the end-to-end experiment, using a ray-tracing algorithm. This tool is the key to prepare CONSERT operation and perform signal analysis. Given a comet shape and a landing site, we have conducted simulations to understand, quantify and get rid of the biases due to the discretization of the shape model.The first inversion using the comet shape model given by OSIRIS and NavCam teams , will assume a propagation in an homogeneous medium. The first goal is to identify and correct artefacts due to the surface interface. The second goal is to evaluate the coherency of the different permittivity estimations given by inverting the latter model on the signal measured at different positions along the orbit. Then it is likely that, based on the first investigations, more sophisticated models (rubble pile, strata) and inversions will be required. A comparative approach between the simulated data and the CONSERT data, will lead to permittivity maps of the nucleus, that are coherent with the observation, with a certain probability. These maps, the first of this type, will provide unprecedented information about the internal structure, the accretion history and the nucleus time evolution.
SU-E-J-161: Inverse Problems for Optical Parameters in Laser Induced Thermal Therapy
Fahrenholtz, SJ; Stafford, RJ; Fuentes, DT
2014-06-01
Purpose: Magnetic resonance-guided laser-induced thermal therapy (MRgLITT) is investigated as a neurosurgical intervention for oncological applications throughout the body by active post market studies. Real-time MR temperature imaging is used to monitor ablative thermal delivery in the clinic. Additionally, brain MRgLITT could improve through effective planning for laser fiber's placement. Mathematical bioheat models have been extensively investigated but require reliable patient specific physical parameter data, e.g. optical parameters. This abstract applies an inverse problem algorithm to characterize optical parameter data obtained from previous MRgLITT interventions. Methods: The implemented inverse problem has three primary components: a parameter-space search algorithm, a physics model, and training data. First, the parameter-space search algorithm uses a gradient-based quasi-Newton method to optimize the effective optical attenuation coefficient, μ-eff. A parameter reduction reduces the amount of optical parameter-space the algorithm must search. Second, the physics model is a simplified bioheat model for homogeneous tissue where closed-form Green's functions represent the exact solution. Third, the training data was temperature imaging data from 23 MRgLITT oncological brain ablations (980 nm wavelength) from seven different patients. Results: To three significant figures, the descriptive statistics for μ-eff were 1470 m{sup −1} mean, 1360 m{sup −1} median, 369 m{sup −1} standard deviation, 933 m{sup −1} minimum and 2260 m{sup −1} maximum. The standard deviation normalized by the mean was 25.0%. The inverse problem took <30 minutes to optimize all 23 datasets. Conclusion: As expected, the inferred average is biased by underlying physics model. However, the standard deviation normalized by the mean is smaller than literature values and indicates an increased precision in the characterization of the optical parameters needed to plan MRg
Direct and inverse theorems on approximation by root functions of a regular boundary-value problem
NASA Astrophysics Data System (ADS)
Radzievskii, G. V.
2006-08-01
One considers the spectral problem x^{(n)}+ Fx=\\lambda x with boundary conditions U_j(x)=0, j=1,\\dots,n, for functions x on \\lbrack0,1\\rbrack . It is assumed that F is a linear bounded operator from the Hölder space C^\\gamma, \\gamma\\in \\lbrack0,n-1), into L_1 and the U_j are bounded linear functionals on C^{k_j} with k_j\\in \\{0,\\dots,n- 1\\}. Let \\mathfrak{P}_\\zeta be the linear span of the root functions of the problem x^{(n)}+ Fx=\\lambda x, U_j(x)=0, j=1,\\dots,n, corresponding to the eigenvalues \\lambda_k with \\vert\\lambda_k\\vert< \\zeta^n, and let \\mathscr E_\\zeta(f)_{W_p^l}:=\\inf\\bigl\\{\\Vert f-g\\Vert _{W_p^l}:g\\in\\mathfrak{P}_\\zeta\\bigr\\}. An estimate of \\mathscr E_\\zeta(f)_{W_p^l} is obtained in terms of the K-functional K(\\zeta^{-m},f;W_p^l,W_{p,U}^{l+m}):= \\kern24pt\\inf\\bigl\\{\\Vert f-x\\Vert _{W_p^l} +\\zeta^{-m}\\Vert x\\Vert _{W_p^{l+m}}:x\\inW_p^{l+m},\\ U_j(x)=0 for k_j (the direct theorem) and an estimate of this K-functional is obtained in terms of \\mathscr E_\\xi(f)_{W_p^l} for \\xi\\le\\zeta (the inverse theorem).In several cases two-sided bounds of the K-functional are found in terms of appropriate moduli of continuity, and then the direct and the inverse theorems are stated in terms of moduli of continuity. For the spectral problem x^{(n)}=\\lambda x with periodic boundary conditions these results coincide with Jackson's and Bernstein's direct and inverse theorems on the approximation of functions by a trigonometric system.
Model Reduction of a Transient Groundwater-Flow Model for Bayesian Inverse Problems
NASA Astrophysics Data System (ADS)
Boyce, S. E.; Yeh, W. W.
2011-12-01
A Bayesian inverse problem requires many repeated model simulations to characterize an unknown parameter's posterior probability distribution. It is computationally infeasible to solve a Bayesian inverse problem of a discretized groundwater flow model with a high dimension parameter and state space. Model reduction has been shown to reduce the dimension of a groundwater model by several orders of magnitude and is well suited for Bayesian inverse problems. A projection-based model reduction approach is proposed to reduce the parameter and state dimensions of a groundwater model. Previous work has done this by using a greedy algorithm for the selection of parameter vectors that make up a basis and their corresponding steady-state solutions for a state basis. The proposed method extends this idea to include transient models by assembling sequentially though the greedy algorithm the parameter and state projection bases. The method begins with the parameter basis being a single vector that is equal to one or an accepted series of values. A set of state vectors that are solutions to the groundwater model using this parameter vector at appropriate times is called the parameter snapshot set. The appropriate times for the parameter snapshot set are determined by maximizing the set's minimum singular value. This optimization is a similar to those used in experimental design for maximizing information. The two bases are made orthonormal by a QR decomposition and applied to the full groundwater model to form a reduced model. The parameter basis is increased with a new parameter vector that maximizes the error between the full model and the reduced model at a set of observation times. The new parameter vector represents where the reduced model is least accurate in representing the original full model. The corresponding parameter snapshot set's appropriate times are found using a greedy algorithm. This sequentially chooses times that have maximum error between the full and
NASA Technical Reports Server (NTRS)
Nitsche, Ludwig C.; Nitsche, Johannes M.; Brenner, Howard
1988-01-01
The sedimentation and diffusion of a nonneutrally buoyant Brownian particle in vertical fluid-filled cylinder of finite length which is instantaneously inverted at regular intervals are investigated analytically. A one-dimensional convective-diffusive equation is derived to describe the temporal and spatial evolution of the probability density; a periodicity condition is formulated; the applicability of Fredholm theory is established; and the parameter-space regions are determined within which the existence and uniqueness of solutions are guaranteed. Numerical results for sample problems are presented graphically and briefly characterized.
Tuan, P.C.; Ju, M.C.
2000-03-01
A novel adaptive and robust input estimation inverse methodology of estimating the time-varying unknown heat flux, named as the input, on the two active boundaries of a 2-D inverse heat conduction problem is presented. The algorithm includes using the Kalman filter to propose a regression model between the residual innovation and the two thermal unknown boundaries flux through given 2-D heat conduction state-space models and noisy measurement sequence. Based on this regression equation, a recursive least-square estimator (RLSE) weighted by the forgetting factor is proposed to on-line estimate these unknowns. The adaptive and robust weighting technique is essential since unknowns input are time-varied and have unpredictable changing status. In this article, the authors provide the bandwidth analysis together with bias and variance tests to construct an efficient and robust forgetting factor as the ratio between the standard deviation of measurement and observable bias innovation at each time step. Herein, the unknowns are robustly and adaptively estimated under the system involving measurement noise, process error, and unpredictable change status of time-varying unknowns. The capabilities of the proposed algorithm are demonstrated through the comparison with the conventional input estimation algorithm and validated by two benchmark performance tests in 2-D cases. Results show that the proposed algorithm not only exhibits superior robust capability but also enhances the estimation performance and highly facilitates practical implementation.
Baghani, Ali; Salcudean, Septimiu; Honarvar, Mohammad; Sahebjavaher, Ramin S; Rohling, Robert; Sinkus, Ralph
2011-08-01
In this paper, a novel approach to the problem of elasticity reconstruction is introduced. In this approach, the solution of the wave equation is expanded as a sum of waves travelling in different directions sharing a common wave number. In particular, the solutions for the scalar and vector potentials which are related to the dilatational and shear components of the displacement respectively are expanded as sums of travelling waves. This solution is then used as a model and fitted to the measured displacements. The value of the shear wave number which yields the best fit is then used to find the elasticity at each spatial point. The main advantage of this method over direct inversion methods is that, instead of taking the derivatives of noisy measurement data, the derivatives are taken on the analytical model. This improves the results of the inversion. The dilatational and shear components of the displacement can also be computed as a byproduct of the method, without taking any derivatives. Experimental results show the effectiveness of this technique in magnetic resonance elastography. Comparisons are made with other state-of-the-art techniques. PMID:21813354
Hybrid modeling of spatial continuity for application to numerical inverse problems
Friedel, Michael J.; Iwashita, Fabio
2013-01-01
A novel two-step modeling approach is presented to obtain optimal starting values and geostatistical constraints for numerical inverse problems otherwise characterized by spatially-limited field data. First, a type of unsupervised neural network, called the self-organizing map (SOM), is trained to recognize nonlinear relations among environmental variables (covariates) occurring at various scales. The values of these variables are then estimated at random locations across the model domain by iterative minimization of SOM topographic error vectors. Cross-validation is used to ensure unbiasedness and compute prediction uncertainty for select subsets of the data. Second, analytical functions are fit to experimental variograms derived from original plus resampled SOM estimates producing model variograms. Sequential Gaussian simulation is used to evaluate spatial uncertainty associated with the analytical functions and probable range for constraining variables. The hybrid modeling of spatial continuity is demonstrated using spatially-limited hydrologic measurements at different scales in Brazil: (1) physical soil properties (sand, silt, clay, hydraulic conductivity) in the 42 km2 Vargem de Caldas basin; (2) well yield and electrical conductivity of groundwater in the 132 km2 fractured crystalline aquifer; and (3) specific capacity, hydraulic head, and major ions in a 100,000 km2 transboundary fractured-basalt aquifer. These results illustrate the benefits of exploiting nonlinear relations among sparse and disparate data sets for modeling spatial continuity, but the actual application of these spatial data to improve numerical inverse modeling requires testing.
An episodic memory-based solution for the acoustic-to-articulatory inversion problem.
Demange, Sébastien; Ouni, Slim
2013-05-01
This paper presents an acoustic-to-articulatory inversion method based on an episodic memory. An episodic memory is an interesting model for two reasons. First, it does not rely on any assumptions about the mapping function but rather it relies on real synchronized acoustic and articulatory data streams. Second, the memory inherently represents the real articulatory dynamics as observed. It is argued that the computational models of episodic memory, as they are usually designed, cannot provide a satisfying solution for the acoustic-to-articulatory inversion problem due to the insufficient quantity of training data. Therefore, an episodic memory is proposed, called generative episodic memory (G-Mem), which is able to produce articulatory trajectories that do not belong to the set of episodes the memory is based on. The generative episodic memory is evaluated using two electromagnetic articulography corpora: one for English and one for French. Comparisons with a codebook-based method and with a classical episodic memory (which is termed concatenative episodic memory) are presented in order to evaluate the proposed generative episodic memory in terms of both its modeling of articulatory dynamics and its generalization capabilities. The results show the effectiveness of the method where an overall root-mean-square error of 1.65 mm and a correlation of 0.71 are obtained for the G-Mem method. They are comparable to those of methods recently proposed. PMID:23654397
NASA Astrophysics Data System (ADS)
Hetmaniok, Edyta
2015-08-01
In this paper the procedure for solving the inverse problem for the binary alloy solidification in the casting mould is presented. Proposed approach is based on the mathematical model suitable for describing the investigated solidification process, the lever arm model describing the macrosegregation process, the finite element method for solving the direct problem and the artificial bee colony algorithm for minimizing the functional expressing the error of approximate solution. Goal of the discussed inverse problem is the reconstruction of heat transfer coefficient and distribution of temperature in investigated region on the basis of known measurements of temperature.
NASA Astrophysics Data System (ADS)
Hetmaniok, Edyta
2016-07-01
In this paper the procedure for solving the inverse problem for the binary alloy solidification in the casting mould is presented. Proposed approach is based on the mathematical model suitable for describing the investigated solidification process, the lever arm model describing the macrosegregation process, the finite element method for solving the direct problem and the artificial bee colony algorithm for minimizing the functional expressing the error of approximate solution. Goal of the discussed inverse problem is the reconstruction of heat transfer coefficient and distribution of temperature in investigated region on the basis of known measurements of temperature.
NASA Astrophysics Data System (ADS)
Khan, Junaid Ali; Zahoor Raja, Muhammad Asif; Rashidi, Mohammad Mehdi; Syam, Muhammad Ibrahim; Majid Wazwaz, Abdul
2015-10-01
In this research, the well-known non-linear Lane-Emden-Fowler (LEF) equations are approximated by developing a nature-inspired stochastic computational intelligence algorithm. A trial solution of the model is formulated as an artificial feed-forward neural network model containing unknown adjustable parameters. From the LEF equation and its initial conditions, an energy function is constructed that is used in the algorithm for the optimisation of the networks in an unsupervised way. The proposed scheme is tested successfully by applying it on various test cases of initial value problems of LEF equations. The reliability and effectiveness of the scheme are validated through comprehensive statistical analysis. The obtained numerical results are in a good agreement with their corresponding exact solutions, which confirms the enhancement made by the proposed approach.
NASA Astrophysics Data System (ADS)
Stas'kov, N. I.; Shulga, A. V.
2016-03-01
An analytical solution of the inverse spectroscopic ellipsometry problem for a transparent layer on an absorbing substrate is obtained based on the envelope function approximation. It is used to determine the spectral dependences of the refractive index n(λ) and absorption k(λ) for model and real silicon substrates with silicon dioxide layers. It was possible to determine analytically the effective optical characteristics of the silicon substrate in the approximation of a layer-substrate model with ideal interface boundaries. This result, together with the relative positions of the spectral curves for the ellipsometric angles tan ψ(λ) and cos Δ(λ) measured for a silicon substrate with a natural surface layer and for a silicon dioxide layer-silicon substrate structure, can be explained by the fact that the silicon dioxide layer is surrounded by transition and surface layers.
Mohan, R.S.; Kovacevic, R.; Beardsley, H.E.
1996-12-31
In abrasive waterjet (AWJ) cutting, the cutting tool is a thin stream of high velocity abrasive waterjet slurry which can be considered as a moving line heat source that increases the temperature of the narrow zone along the cut kerf wall. A suitably defined inverse heat conduction problem which uses the experimentally determined temperature histories at various points in the workpiece, is adopted to determine the heat flux at the cutting zone. Temperature distribution in the workpiece and the cutting nozzle during AWJ cutting is monitored using infrared thermography. A suitable strategy for on-line monitoring of the radial and axial wear of the AWJ nozzle based on the nozzle temperature distribution is also proposed.
Isotropic inverse-problem approach for two-dimensional phase unwrapping.
Kamilov, Ulugbek S; Papadopoulos, Ioannis N; Shoreh, Morteza H; Psaltis, Demetri; Unser, Michael
2015-06-01
We propose a new technique for two-dimensional phase unwrapping. The unwrapped phase is found as the solution of an inverse problem that consists in the minimization of an energy functional. The latter includes a weighted data fidelity term that favors sparsity in the error between the true and wrapped phase differences, as well as a regularizer based on higher-order total variation. One desirable feature of our method is its rotation invariance, which allows it to unwrap a much larger class of images compared to the state of the art. We demonstrate the effectiveness of our method through several experiments on simulated and real data obtained through the tomographic phase microscope. The proposed method can enhance the applicability and outreach of techniques that rely on quantitative phase evaluation. PMID:26367043
Presymplectic current and the inverse problem of the calculus of variations
Khavkine, Igor
2013-11-15
The inverse problem of the calculus of variations asks whether a given system of partial differential equations (PDEs) admits a variational formulation. We show that the existence of a presymplectic form in the variational bicomplex, when horizontally closed on solutions, allows us to construct a variational formulation for a subsystem of the given PDE. No constraints on the differential order or number of dependent or independent variables are assumed. The proof follows a recent observation of Bridges, Hydon, and Lawson [Math. Proc. Cambridge Philos. Soc. 148(01), 159–178 (2010)] and generalizes an older result of Henneaux [Ann. Phys. 140(1), 45–64 (1982)] from ordinary differential equations (ODEs) to PDEs. Uniqueness of the variational formulation is also discussed.
NASA Astrophysics Data System (ADS)
Raynaud, M.; Bransier, J.
A space-marching finite difference algorithm is developed for solving the one-dimensional inverse heat conduction problem. The method is easy to apply, stable, and as accurate as the most efficient existing methods. An experimental set-up made of a rectangular parallelepiped polymerized around a woof of thermocouples has been designed especially to validate the method. The thermal conductivity of the test specimen was previously determined with the same set-up, and the specific heat is estimated during the experiments. The estimated surface heat flux is in very good agreement with the heat flux measured by a foil heat flux gage, regardless of the sensor locations. These results show that the method remains effective in spite of the cumulated effects of the errors due to the data acquisition system, to the location and calibration of the sensors, and to the simultaneous estimation of the specific heat.
Control of plasma profile in microwave discharges via inverse-problem approach
Yasaka, Yasuyoshi; Tobita, Naoki; Tsuji, Akihiro
2013-12-15
In the manufacturing process of semiconductors, plasma processing is an essential technology, and the plasma used in the process is required to be of high density, low temperature, large diameter, and high uniformity. This research focuses on the microwave-excited plasma that meets these needs, and the research target is a spatial profile control. Two novel techniques are introduced to control the uniformity; one is a segmented slot antenna that can change radial distribution of the radiated field during operation, and the other is a hyper simulator that can predict microwave power distribution necessary for a desired radial density profile. The control system including these techniques provides a method of controlling radial profiles of the microwave plasma via inverse-problem approach, and is investigated numerically and experimentally.
An inverse problem design method for branched and unbranched axially symmetrical ducts
NASA Technical Reports Server (NTRS)
Nelson, C. D.; Yang, T.
1976-01-01
This paper concerns the potential flow design of axially symmetrical ducts of both circular and annular cross section with or without wall suction or blowing slots. The objective of the work was to develop a method by which such ducts could be designed with directly prescribed wall pressure variation. Previous axially symmetrical design methods applied only to circular cross sectional ducts and required that the pressure distribution be prescribed along the duct centerline and not along the duct wall. The present method uses an inverse problem approach which extends the method of Stanitz to the axially symmetrical case, and an approximation is used to account for the stagnation point in branched duct designs. Two examples of successful designs of diffusers with suction slots are presented.
Regularization strategy for an inverse problem for a 1 + 1 dimensional wave equation
NASA Astrophysics Data System (ADS)
Korpela, Jussi; Lassas, Matti; Oksanen, Lauri
2016-06-01
An inverse boundary value problem for a 1 + 1 dimensional wave equation with a wave speed c(x) is considered. We give a regularization strategy for inverting the map { A } :c\\mapsto {{Λ }}, where Λ is the hyperbolic Neumann-to-Dirichlet map corresponding to the wave speed c. That is, we consider the case when we are given a perturbation of the Neumann-to-Dirichlet map \\tilde{{{Λ }}}={{Λ }}+{ E }, where { E } corresponds to the measurement errors, and reconstruct an approximative wave speed \\tilde{c}. We emphasize that \\tilde{{{Λ }}} may not be in the range of the map { A }. We show that the reconstructed wave speed \\tilde{c} satisfies \\parallel \\tilde{c}-c\\parallel ≤slant C\\parallel { E }{\\parallel }1/54. Our regularization strategy is based on a new formula to compute c from Λ.
An inverse problem for reduced-encoding MRI velocimetry in potential flow.
Raguin, L Guy; Kodali, Anil K; Rovas, Dimitrios V; Georgiadis, John G
2004-01-01
We propose a computational technique to reconstruct internal physiological flows described by sparse point-wise MRI velocity measurements. Assuming that the viscous forces in the flow are negligible, the incompressible flow field can be obtained from a velocity potential that satisfies Laplace's equation. A set of basis functions each satisfying Laplace's equation with appropriately defined boundary data is constructed using the finite-element method. An inverse problem is formulated where higher resolution boundary and internal velocity data are extracted from the point-wise MRI velocity measurements using a least-squares method. From the results we obtained with approximately 100 internal measurement points, the proposed reconstruction method is shown to be effective in filtering out the experimental noise at levels as high as 30%, while matching the reference solution within 2%. This allows the reconstruction of a high-resolution velocity field with limited MRI encoding. PMID:17271875
Inverse heat transfer problem in digital temperature control in plate fin and tube heat exchangers
NASA Astrophysics Data System (ADS)
Taler, Dawid; Sury, Adam
2011-12-01
The aim of the paper is a steady-state inverse heat transfer problem for plate-fin and tube heat exchangers. The objective of the process control is to adjust the number of fan revolutions per minute so that the water temperature at the heat exchanger outlet is equal to a preset value. Two control techniques were developed. The first is based on the presented mathematical model of the heat exchanger while the second is a digital proportional-integral-derivative (PID) control. The first procedure is very stable. The digital PID controller becomes unstable if the water volumetric flow rate changes significantly. The developed techniques were implemented in digital control system of the water exit temperature in a plate fin and tube heat exchanger. The measured exit temperature of the water was very close to the set value of the temperature if the first method was used. The experiments showed that the PID controller works also well but becomes frequently unstable.
Thomas, Edward V.; Stork, Christopher L.; Mattingly, John K.
2015-07-01
Inverse radiation transport focuses on identifying the configuration of an unknown radiation source given its observed radiation signatures. The inverse problem is traditionally solved by finding the set of transport model parameter values that minimizes a weighted sum of the squared differences by channel between the observed signature and the signature pre dicted by the hypothesized model parameters. The weights are inversely proportional to the sum of the variances of the measurement and model errors at a given channel. The traditional implicit (often inaccurate) assumption is that the errors (differences between the modeled and observed radiation signatures) are independent across channels. Here, an alternative method that accounts for correlated errors between channels is described and illustrated using an inverse problem based on the combination of gam ma and neutron multiplicity counting measurements.
S-Genius, a universal software platform with versatile inverse problem resolution for scatterometry
NASA Astrophysics Data System (ADS)
Fuard, David; Troscompt, Nicolas; El Kalyoubi, Ismael; Soulan, Sébastien; Besacier, Maxime
2013-05-01
S-Genius is a new universal scatterometry platform, which gathers all the LTM-CNRS know-how regarding the rigorous electromagnetic computation and several inverse problem solver solutions. This software platform is built to be a userfriendly, light, swift, accurate, user-oriented scatterometry tool, compatible with any ellipsometric measurements to fit and any types of pattern. It aims to combine a set of inverse problem solver capabilities — via adapted Levenberg- Marquard optimization, Kriging, Neural Network solutions — that greatly improve the reliability and the velocity of the solution determination. Furthermore, as the model solution is mainly vulnerable to materials optical properties, S-Genius may be coupled with an innovative material refractive indices determination. This paper will a little bit more focuses on the modified Levenberg-Marquardt optimization, one of the indirect method solver built up in parallel with the total SGenius software coding by yours truly. This modified Levenberg-Marquardt optimization corresponds to a Newton algorithm with an adapted damping parameter regarding the definition domains of the optimized parameters. Currently, S-Genius is technically ready for scientific collaboration, python-powered, multi-platform (windows/linux/macOS), multi-core, ready for 2D- (infinite features along the direction perpendicular to the incident plane), conical, and 3D-features computation, compatible with all kinds of input data from any possible ellipsometers (angle or wavelength resolved) or reflectometers, and widely used in our laboratory for resist trimming studies, etching features characterization (such as complex stack) or nano-imprint lithography measurements for instance. The work about kriging solver, neural network solver and material refractive indices determination is done (or about to) by other LTM members and about to be integrated on S-Genius platform.
NASA Astrophysics Data System (ADS)
Shojaeefard, M. H.; Goudarzi, K.; Mazidi, M. Sh.
2009-06-01
The problems involving periodic contacting surfaces have different practical applications. An inverse heat conduction problem for estimating the periodic Thermal Contact Conductance (TCC) between one-dimensional, constant property contacting solids has been investigated with conjugate gradient method (CGM) of function estimation. This method converges very rapidly and is not so sensitive to the measurement errors. The advantage of the present method is that no a priori information is needed on the variation of the unknown quantities, since the solution automatically determines the functional form over the specified domain. A simple, straight forward technique is utilized to solve the direct, sensitivity and adjoint problems, in order to overcome the difficulties associated with numerical methods. Two general classes of results, the results obtained by applying inexact simulated measured data and the results obtained by using data taken from an actual experiment are presented. In addition, extrapolation method is applied to obtain actual results. Generally, the present method effectively improves the exact TCC when exact and inexact simulated measurements input to the analysis. Furthermore, the results obtained with CGM and the extrapolation results are in agreement and the little deviations can be negligible.
Massively parallel solution of the inverse scattering problem for integrated circuit quality control
Leland, R.W.; Draper, B.L.; Naqvi, S.; Minhas, B.
1997-09-01
The authors developed and implemented a highly parallel computational algorithm for solution of the inverse scattering problem generated when an integrated circuit is illuminated by laser. The method was used as part of a system to measure diffraction grating line widths on specially fabricated test wafers and the results of the computational analysis were compared with more traditional line-width measurement techniques. The authors found they were able to measure the line width of singly periodic and doubly periodic diffraction gratings (i.e. 2D and 3D gratings respectively) with accuracy comparable to the best available experimental techniques. They demonstrated that their parallel code is highly scalable, achieving a scaled parallel efficiency of 90% or more on typical problems running on 1024 processors. They also made substantial improvements to the algorithmics and their original implementation of Rigorous Coupled Waveform Analysis, the underlying computational technique. These resulted in computational speed-ups of two orders of magnitude in some test problems. By combining these algorithmic improvements with parallelism the authors achieve speedups of between a few thousand and hundreds of thousands over the original engineering code. This made the laser diffraction measurement technique practical.
Double obstacle phase field approach to an inverse problem for a discontinuous diffusion coefficient
NASA Astrophysics Data System (ADS)
Deckelnick, Klaus; Elliott, Charles M.; Styles, Vanessa
2016-04-01
We propose a double obstacle phase field approach to the recovery of piece-wise constant diffusion coefficients for elliptic partial differential equations. The approach to this inverse problem is that of optimal control in which we have a quadratic fidelity term to which we add a perimeter regularization weighted by a parameter σ. This yields a functional which is optimized over a set of diffusion coefficients subject to a state equation which is the underlying elliptic PDE. In order to derive a problem which is amenable to computation the perimeter functional is relaxed using a gradient energy functional together with an obstacle potential in which there is an interface parameter ɛ. This phase field approach is justified by proving {{Γ }}- convergence to the functional with perimeter regularization as ε \\to 0. The computational approach is based on a finite element approximation. This discretization is shown to converge in an appropriate way to the solution of the phase field problem. We derive an iterative method which is shown to yield an energy decreasing sequence converging to a discrete critical point. The efficacy of the approach is illustrated with numerical experiments.
Davidson, Susan E; Gillespie, Catherine; Allum, William H; Swarbrick, Edwin
2011-01-01
Backgound The number of patients with chronic gastrointestinal (GI) symptoms after cancer therapies which have a moderate or severe impact on quality of life is similar to the number diagnosed with inflammatory bowel disease annually. However, in contrast to patients with inflammatory bowel disease, most of these patients are not referred for gastroenterological assessment. Clinicians who do see these patients are often unaware of the benefits of targeted investigation (which differ from those required to exclude recurrent cancer), the range of available treatments and how the pathological processes underlying side effects of cancer treatment differ from those in benign GI disorders. This paper aims to help clinicians become aware of the problem and suggests ways in which the panoply of syndromes can be managed. Methods A multidisciplinary literature review was performed to develop guidance to facilitate clinical management of GI side effects of cancer treatments. Results Different pathological processes within the GI tract may produce identical symptoms. Optimal management requires appropriate investigations and coordinated multidisciplinary working. Lactose intolerance, small bowel bacterial overgrowth and bile acid malabsorption frequently develop during or after chemotherapy. Toxin-negative Clostridium difficile and cytomegalovirus infection may be fulminant in immunosuppressed patients and require rapid diagnosis and treatment. Hepatic side effects include reactivation of viral hepatitis, sinusoidal obstruction syndrome, steatosis and steatohepatitis. Anticancer biological agents have multiple interactions with conventional drugs. Colonoscopy is contraindicated in neutropenic enterocolitis but endoscopy may be life-saving in other patients with GI bleeding. After cancer treatment, simple questions can identify patients who need referral for specialist management of GI symptoms. Other troublesome pelvic problems (eg, urinary, sexual, nutritional) are frequent
NASA Astrophysics Data System (ADS)
Kanguzhin, Baltabek; Tokmagambetov, Niyaz
2016-08-01
In this work, we research a boundary inverse problem of spectral analysis of a differential operator with integral boundary conditions in the functional space L2(0, b) where b < ∞. A uniqueness theorem of the inverse boundary problem in L2(0, b) is proved. Note that a boundary inverse problem of spectral analysis is the problem of recovering boundary conditions of the operator by its spectrum and some additional data.
NASA Astrophysics Data System (ADS)
Pham, H. V.; Elshall, A. S.; Tsai, F. T.; Yan, L.
2012-12-01
The inverse problem in groundwater modeling deals with a rugged (i.e. ill-conditioned and multimodal), nonseparable and noisy function since it involves solving second order nonlinear partial deferential equations with forcing terms. Derivative-based optimization algorithms may fail to reach a near global solution due to their stagnation at a local minimum solution. To avoid entrapment in a local optimum and enhance search efficiency, this study introduces the covariance matrix adaptation-evolution strategy (CMA-ES) as a local derivative-free optimization method. In the first part of the study, we compare CMA-ES with five commonly used heuristic methods and the traditional derivative-based Gauss-Newton method on a hypothetical problem. This problem involves four different cases to allow a rigorous assessment against ten criterions: ruggedness in terms of nonsmooth and multimodal, ruggedness in terms of ill-conditioning and high nonlinearity, nonseparablity, high dimensionality, noise, algorithm adaptation, algorithm tuning, performance, consistency, parallelization (scaling with number of cores) and invariance (solution vector and function values). The CMA-ES adapts a covariance matrix representing the pair-wise dependency between decision variables, which approximates the inverse of the Hessian matrix up to a certain factor. The solution is updated with the covariance matrix and an adaptable step size, which are adapted through two conjugates that implement heuristic control terms. The covariance matrix adaptation uses information from the current population of solutions and from the previous search path. Since such an elaborate search mechanism is not common in the other heuristic methods, CMA-ES proves to be more robust than other population-based heuristic methods in terms of reaching a near-optimal solution for a rugged, nonseparable and noisy inverse problem. Other favorable properties that the CMA-ES exhibits are the consistency of the solution for repeated
A 2D inverse problem of predicting boiling heat transfer in a long fin
NASA Astrophysics Data System (ADS)
Orzechowski, Tadeusz
2015-12-01
A method for the determination of local values of the heat transfer coefficient on non-isothermal surfaces was analyzed on the example of a long smooth-surfaced fin made of aluminium. On the basis of the experimental data, two cases were taken into consideration: one-dimensional model for Bi < 0.1 and two-dimensional model for thicker elements. In the case when the drop in temperature over the thickness could be omitted, the rejected local values of heat fluxes were calculated from the integral of the equation describing temperature distribution on the fin. The corresponding boiling curve was plotted on the basis of temperature gradient distribution as a function of superheat. For thicker specimens, where Bi > 0.1, the problem was modelled using a 2-D heat conduction equation, for which the boundary conditions were posed on the surface observed with a thermovision camera. The ill-conditioned inverse problem was solved using a method of heat polynomials, which required validation.
NASA Astrophysics Data System (ADS)
Ramezanpour, A.
2016-06-01
We study the inverse problem of constructing an appropriate Hamiltonian from a physically reasonable set of orthogonal wave functions for a quantum spin system. Usually, we are given a local Hamiltonian and our goal is to characterize the relevant wave functions and energies (the spectrum) of the system. Here, we take the opposite approach; starting from a reasonable collection of orthogonal wave functions, we try to characterize the associated parent Hamiltonians, to see how the wave functions and the energy values affect the structure of the parent Hamiltonian. Specifically, we obtain (quasi) local Hamiltonians by a complete set of (multilayer) product states and a local mapping of the energy values to the wave functions. On the other hand, a complete set of tree wave functions (having a tree structure) results to nonlocal Hamiltonians and operators which flip simultaneously all the spins in a single branch of the tree graph. We observe that even for a given set of basis states, the energy spectrum can significantly change the nature of interactions in the Hamiltonian. These effects can be exploited in a quantum engineering problem optimizing an objective functional of the Hamiltonian.
An inverse problem of determining the implied volatility in option pricing
NASA Astrophysics Data System (ADS)
Deng, Zui-Cha; Yu, Jian-Ning; Yang, Liu
2008-04-01
In the Black-Scholes world there is the important quantity of volatility which cannot be observed directly but has a major impact on the option value. In practice, traders usually work with what is known as implied volatility which is implied by option prices observed in the market. In this paper, we use an optimal control framework to discuss an inverse problem of determining the implied volatility when the average option premium, namely the average value of option premium corresponding with a fixed strike price and all possible maturities from the current time to a chosen future time, is known. The issue is converted into a terminal control problem by Green function method. The existence and uniqueness of the minimum of the control functional are addressed by the optimal control method, and the necessary condition which must be satisfied by the minimum is also given. The results obtained in the paper may be useful for those who engage in risk management or volatility trading.
Inverse scattering for an exterior Dirichlet problem. [due to metallic cylinder
NASA Technical Reports Server (NTRS)
Hariharan, S. I.
1982-01-01
Scattering caused by a metallic cylinder in the field of a wire carrying a periodic current is studied, with a view to determining the location and shape of the cylinder in light of far field measurements between the cylinder and the wire. The associated direct problem is the exterior Dirichlet problem for the Helmholtz equation in two dimensions, and an improved low frequency estimate for its solution by integral equation methods is shown by inverse scattering calculations to be accurate to this estimate. The far field measurements are related to low frequency boundary integral equations whose solutions may be expressed in terms of a mapping function for the exterior of the unknown curve onto the exterior of a unit disk. The conformal transformation's Laurent expansion coefficients can be related to those of the far field, the first of which leads to the calculation of the distance between the source and the cylinder, while the other coefficients are determined by placing the source in a different location.
Radiative transport in plant canopies: Forward and inverse problem for UAV applications
NASA Astrophysics Data System (ADS)
Furfaro, Roberto
This dissertation deals with modeling the radiative regime in vegetation canopies and the possible remote sensing applications derived by solving the forward and inverse canopy transport equation. The aim of the research is to develop a methodology (called "end-to-end problem solution") that, starting from first principles describing the interaction between light and vegetation, constructs, as the final product, a tool that analyzes remote sensing data for precision agriculture (ripeness prediction). The procedure begins by defining the equations that describe the transport of photons inside the leaf and within the canopy. The resulting integro-differential equations are numerically integrated by adapting the conventional discrete-ordinate methods to compute the reflectance at the top of the canopy. The canopy transport equation is also analyzed to explore its spectral properties. The goal here is to apply Case's method to determine eigenvalues and eigenfunctions and to prove completeness. A model inversion is attempted by using neural network algorithms. Using input-outputs generated by running the forward model, a neural network is trained to learn the inverse map. The model-based neural network represents the end product of the overall procedure. During Oct 2002, an Unmanned Aerial Vehicles (UAVs) equipped with a camera system, flew over Kauai to take images of coffee field plantations. Our goal is to predict the amount of ripe coffee cherries for optimal harvesting. The Leaf-Canopy model was modified to include cherries as absorbing and scattering elements and two classes of neural networks were trained on the model to learn the relationship between reflectance and percentage of ripe, over-ripe and under-ripe cherries. The neural networks are interfaced with images coming from Kauai to predict ripeness percentage. Both ground and airborne images are considered. The latter were taken from the on-board Helios UAV camera system flying over the Kauai coffee field
NASA Astrophysics Data System (ADS)
Yang, Li; Wang, Ye; Liu, Huikai; Yan, Guanghui; Kou, Wei
2014-11-01
The components overheating inside an object, such as inside an electric control cabinet, a moving object, and a running machine, can easily lead to equipment failure or fire accident. The infrared remote sensing method is used to inspect the surface temperature of object to identify the overheating components inside the object in recent years. It has important practical application of using infrared thermal imaging surface temperature measurement to identify the internal overheating elements inside an electric control cabinet. In this paper, through the establishment of test bench of electric control cabinet, the experimental study was conducted on the inverse identification technology of internal overheating components inside an electric control cabinet using infrared thermal imaging. The heat transfer model of electric control cabinet was built, and the temperature distribution of electric control cabinet with internal overheating element is simulated using the finite volume method (FVM). The outer surface temperature of electric control cabinet was measured using the infrared thermal imager. Combining the computer image processing technology and infrared temperature measurement, the surface temperature distribution of electric control cabinet was extracted, and using the identification algorithm of inverse heat transfer problem (IHTP) the position and temperature of internal overheating element were identified. The results obtained show that for single element overheating inside the electric control cabinet the identifying errors of the temperature and position were 2.11% and 5.32%. For multiple elements overheating inside the electric control cabinet the identifying errors of the temperature and positions were 3.28% and 15.63%. The feasibility and effectiveness of the method of IHTP and the correctness of identification algorithm of FVM were validated.
Circulation of the Carribean Sea: a well-resolved inverse problem
Roemmich, D.
1981-09-20
The Caribbean Sea is selected as a region where the large-scale circulation is well determined by historical hydrographic measurements through application of the inverse method. A simple example is used to illustrate the technique and to demonstrate how some physically relevant quantities may be well determined in the formally underdetermined inverse problem. The geostrophic flow field in the Caribbean is found by imposing mass and salt conservation constraints in seven layers separated by surfaces of constant potential density. An unsmoothed solution is displayed that has weak dependence on an initial choice of reference level. In addition, a unique smoothed solution is shown. Above sigma/sub theta/ = 27.4, the total flow leaving the western Caribbean is estimated to be 29 x 10/sup 6/ m/sup 3/ s/sup -1/, in agreement with direct measurements. This flow is made up of 22 x 10/sup 6/ m/sup 3/s /sup -1/ entering the Caribbean from the east and flowing across the southern half of the basin as the Caribbean Current, and 7 x 10/sup 6/ m/sup 3/ s/sup -1/, in agreement with direct measurements. This flow is made up of 22 x 10/sup 6/ m/sup 3/ s/sup -1/ entering the Caribbean from the east and flowing across the southern half of the basin as the Caribbean Current, and 7 x 10/sup 7/ m/sup 3/ s/sup -1/ entering from the north through Windward Passage. Both of these currents show small-scale variability that diminishes with distance from the respective passages. The deep flow has no net transport, as required by the shallow exit, but a well organized clockwise recirculation is found in the deep eastern Caribbean.
NASA Astrophysics Data System (ADS)
Nazarov, L. A.; Nazarova, L. A.; Romenskii, E. I.; Tcheverda, V. A.; Epov, M. I.
2016-02-01
A method for estimating the stress-strain state of a rock massif in the vicinity of underground facilities is substantiated. This method is based on solution of the boundary inverse problem of defining the components of an external stress field from the acoustic sounding data. The acoustic sounding data used are the arrival times of diving head longitudinal waves, recorded in a long mine shaft. Numerical experiments have revealed the optimal arrangement of the recording network and the limited relative error in the input data, which, taken together, provide for solvability of the inverse problem.
Gray, D.B.; McMechan, G.A.
1995-06-01
The analytic solution of the Lippman-Schwinger seismic inverse problem in three spatial dimensions, assuming a point source and a constant-density earth model, is valid in the spatial zero frequency limit. It is expressed as a two-dimensional inverse Fourier transform followed by an inverse Laplace transform. For the case of laterally homogeneous velocity, the analytic solution is correct when applied to a forward solution of the wave equation for a single-interface velocity model. Error surfaces of the non-linear, iterative, least-squares inversions corresponding to multiple, constant-velocity, horizontal layers have an absolute minimum at or near the location of the solution parameters for zero and low frequencies. The error surface for a scattered wavefield dataset generated by 3D finite-difference modeling combined with a priori constraints, produces nearly correct solutions for a range of low frequencies. Thus, this approach has potential for applicability to field data. 24 refs., 7 figs.
NASA Technical Reports Server (NTRS)
Goel, N.; Strebel, D. E.
1983-01-01
An important but relatively uninvestigated problem in remote sensing is the inversion of vegetative canopy reflectance models to obtain agrophysical parameters, given measured reflectances. The problem is here formally defined and its solution outlined. Numerical nonlinear optimization techniques are used to implement this inversion to obtain the leaf area index using Suits' canopy reflectance model. The results for a variety of cases indicate that this can be done successfully using infrared reflectances at different views or azimuth angles or a combination thereof. The other parameters of the model must be known, although reasonable measurement errors can be tolerated without seriously degrading the accuracy of the inversion. The application of the technique to ground based remote-sensing experiments is potentially useful, but is limited to the degree to which the canopy reflectance model can accurately predict observed reflectances.
Model Reduction of a 2-Dimenional Sedimentary Texture Groundwater-Flow Model for Inverse Problems
NASA Astrophysics Data System (ADS)
Boyce, S. E.; Yeh, W.
2013-12-01
pattern by defining the 57,888 elements with a fraction of coarse- and fine-grained sediment. This highly-heterogeneous model is reduced by selecting combinations of the global coarse and fine hydraulic conductivity at a set power of the power mean. The dimensionality of the Texture Oristano model for power-mean powers of 0, 0.8, and 1 are reduced from a system of 29,197 ordinary differential equations to 60, 42, and 45 equations, respectively. The application of the parameter-independent model reduction reduces the CPU time by 90% for a single model run. The robustness of the three reduced models, which differ in the power-mean power, are evaluated by solving a synthetic inverse problem with the BFGS algorithm. The three reduced models produced the same optimal coarse and fine hydraulic conductivity as the full model's result from BFGS. This study demonstrates that parameter-independent model reduction is possible for a highly, heterogeneous groundwater model and the inverse problem can be solved with the reduced model at a fraction of the time required by the full model.
Representation and constraints: the inverse problem and the structure of visual space.
Hatfield, Gary
2003-11-01
Visual space can be distinguished from physical space. The first is found in visual experience, while the second is defined independently of perception. Theorists have wondered about the relation between the two. Some investigators have concluded that visual space is non-Euclidean, and that it does not have a single metric structure. Here it is argued (1) that visual space exhibits contraction in all three dimensions with increasing distance from the observer, (2) that experienced features of this contraction (including the apparent convergence of lines in visual experience that are produced from physically parallel stimuli in ordinary viewing conditions) are not the same as would be the experience of a perspective projection onto a frontoparallel plane, and (3) that such contraction is consistent with size constancy. These properties of visual space are different from those that would be predicted if spatial perception resulted from the successful solution of the inverse problem. They are consistent with the notion that optical constraints have been internalized. More generally, they are also consistent with the notion that visual spatial structures bear a resemblance relation to physical spatial structures. This notion supports a type of representational relation that is distinct from mere causal correspondence. The reticence of some philosophers and psychologists to discuss the structure of phenomenal space is diagnosed in terms of the simple materialism and the functionalism of the 1970s and 1980s. PMID:14670704
Using eigenmodes to perform the inverse problem associated with resonant ultrasound spectroscopy
David Hurley; Farhad Farzbod
2012-11-01
In principle, resonant ultrasonic spectroscopy (RUS) can be used to characterize any parameter that influences the mechanical resonant response of a sample. Examples include the elastic constants, sample dimensions, and crystal orientation. Extracting the parameter of interest involves performing the inverse problem, which typically entails an iterative routine that compares calculated and measured eigenfrequencies. Here, we propose an alternative method based on laser-based resonant ultrasound spectroscopy (LRUS) that uses the eigenmodes. LRUS uses a pulsed laser to thermoelastically excite ultrasound and an interferometer to detect out-of-plane displacement associated with ultrasonic resonances. By raster scanning the probe along the sample surface, an image of the out-ofplane displacement pattern (i.e., eigenmode) is obtained. As an example of this method, we describe a technique to calculate the crystallographic orientation of a single-crystal high-purity copper sample. The crystallographic orientation is computed by comparing theoretical and experimental eigenmodes. The computed angle is shown to be in very good agreement with the angle obtained using electron backscatter diffraction. In addition, a comparison is made using eigenfrequencies and eigenmodes to calculate the crystallographic orientation. It is found for this particular application, the eigenmode method has superior sensitivity to crystal orientation.
Inverse Problem for Color Doppler Ultrasound-Assisted Intracardiac Blood Flow Imaging
Jang, Jaeseong
2016-01-01
For the assessment of the left ventricle (LV), echocardiography has been widely used to visualize and quantify geometrical variations of LV. However, echocardiographic image itself is not sufficient to describe a swirling pattern which is a characteristic blood flow pattern inside LV without any treatment on the image. We propose a mathematical framework based on an inverse problem for three-dimensional (3D) LV blood flow reconstruction. The reconstruction model combines the incompressible Navier-Stokes equations with one-direction velocity component of the synthetic flow data (or color Doppler data) from the forward simulation (or measurement). Moreover, time-varying LV boundaries are extracted from the intensity data to determine boundary conditions of the reconstruction model. Forward simulations of intracardiac blood flow are performed using a fluid-structure interaction model in order to obtain synthetic flow data. The proposed model significantly reduces the local and global errors of the reconstructed flow fields. We demonstrate the feasibility and potential usefulness of the proposed reconstruction model in predicting dynamic swirling patterns inside the LV over a cardiac cycle. PMID:27313657
Schmidt, S; Klein, A E; Paul, T; Gross, H; Diziain, S; Steinert, M; Assafrao, A C; Pertsch, T; Urbach, H P; Rockstuhl, C
2016-02-22
Aperture based scanning near field optical microscopes are important instruments to study light at the nanoscale and to understand the optical functionality of photonic nanostructures. In general, a detected image is affected by both the transverse electric and magnetic field components of light. The discrimination of the individual field components is challenging as these four field components are contained within two signals in the case of a polarization resolved measurement. Here, we develop a methodology to solve the inverse imaging problem and to retrieve the vectorial field components from polarization and phase resolved measurements. Our methodology relies on the discussion of the image formation process in aperture based scanning near field optical microscopes. On this basis, we are also able to explain how the relative contributions of the electric and magnetic field components within detected images depend on the chosen probe. We can therefore also describe the influence of geometrical and material parameters of individual probes within the image formation process. This allows probes to be designed that are primarily sensitive either to the electric or magnetic field components of light. PMID:26907063
General Features of Supersymmetric Signals at the ILC: Solving the LHC Inverse Problem
Berger, Carola F.; Gainer, James S.; Hewett, JoAnne L.; Lillie, Ben; Rizzo, Thomas G.
2007-12-19
We examine whether the {radical}s = 500 GeV International Linear Collider with 80% electron beam polarization can be used to solve the LHC Inverse Problem within the framework of the MSSM. We investigate 242 points in the MSSM parameter space, which we term models, that correspond to the 162 pairs of models found by Arkani-Hamed et al. to give indistinguishable signatures at the LHC. We first determine whether the production of the various SUSY particles is visible above the Standard Model background for each of these parameter space points, and then make a detailed comparison of their various signatures. Assuming an integrated luminosity of 500 fb{sup -1}, we find that only 82 out of 242 models lead to visible signatures of some kind with a significance {ge} 5 and that only 57(63) out of the 162 model pairs are distinguishable at 5(3){sigma}. Our analysis includes PYTHIA and CompHEP SUSY signal generation, full matrix element SM backgrounds for all 2 {yields} 2, 2 {yields} 4, and 2 {yields} 6 processes, ISR and beamstrahlung generated via WHIZARD/GuineaPig, and employs the fast SiD detector simulation org.lcsim.
NASA Astrophysics Data System (ADS)
Bao, Xingxian; Cao, Aixia; Zhang, Jing
2016-07-01
Modal parameters estimation plays an important role for structural health monitoring. Accurately estimating the modal parameters of structures is more challenging as the measured vibration response signals are contaminated with noise. This study develops a mathematical algorithm of solving the partially described inverse singular value problem (PDISVP) combined with the complex exponential (CE) method to estimate the modal parameters. The PDISVP solving method is to reconstruct an L2-norm optimized (filtered) data matrix from the measured (noisy) data matrix, when the prescribed data constraints are one or several sets of singular triplets of the matrix. The measured data matrix is Hankel structured, which is constructed based on the measured impulse response function (IRF). The reconstructed matrix must maintain the Hankel structure, and be lowered in rank as well. Once the filtered IRF is obtained, the CE method can be applied to extract the modal parameters. Two physical experiments, including a steel cantilever beam with 10 accelerometers mounted, and a steel plate with 30 accelerometers mounted, excited by an impulsive load, respectively, are investigated to test the applicability of the proposed scheme. In addition, the consistency diagram is proposed to exam the agreement among the modal parameters estimated from those different accelerometers. Results indicate that the PDISVP-CE method can significantly remove noise from measured signals and accurately estimate the modal frequencies and damping ratios.
Extraction of skin-friction fields from surface flow visualizations as an inverse problem
NASA Astrophysics Data System (ADS)
Liu, Tianshu
2013-12-01
Extraction of high-resolution skin-friction fields from surface flow visualization images as an inverse problem is discussed from a unified perspective. The surface flow visualizations used in this study are luminescent oil-film visualization and heat-transfer and mass-transfer visualizations with temperature- and pressure-sensitive paints (TSPs and PSPs). The theoretical foundations of these global methods are the thin-oil-film equation and the limiting forms of the energy- and mass-transport equations at a wall, which are projected onto the image plane to provide the relationships between a skin-friction field and the relevant quantities measured by using an imaging system. Since these equations can be re-cast in the same mathematical form as the optical flow equation, they can be solved by using the variational method in the image plane to extract relative or normalized skin-friction fields from images. Furthermore, in terms of instrumentation, essentially the same imaging system for measurements of luminescence can be used in these surface flow visualizations. Examples are given to demonstrate the applications of these methods in global skin-friction diagnostics of complex flows.
Estimation of local error by a neural model in an inverse scattering problem
NASA Astrophysics Data System (ADS)
Robert, S.; Mure-Rauvaud, A.; Thiria, S.; Badran, F.
2005-07-01
Characterization of optical gratings by resolution of inverse scattering problem has become a widely used tool. Indeed, it is known as a non-destructive, rapid and non-invasive method in opposition with microscopic characterizations. Use of a neural model is generally implemented and has shown better results by comparison with other regression methods. The neural network learns the relationship between the optical signature and the corresponding profile shape. The performance of such a non-linear regression method is usually estimated by the root mean square error calculated on a data set not involved in the training process. However, this error estimation is not very significant and tends to flatten the error in the different areas of variable space. We introduce, in this paper, the calculation of local error for each geometrical parameter representing the profile shape. For this purpose a second neural network is implemented to learn the variance of results obtained by the first one. A comparison with the root mean square error confirms a gain of local precision. Finally, the method is applied in the optical characterization of a semi-conductor grating with a 1 μ m period.
An algorithmic framework for Mumford-Shah regularization of inverse problems in imaging
NASA Astrophysics Data System (ADS)
Hohm, Kilian; Storath, Martin; Weinmann, Andreas
2015-11-01
The Mumford-Shah model is a very powerful variational approach for edge preserving regularization of image reconstruction processes. However, it is algorithmically challenging because one has to deal with a non-smooth and non-convex functional. In this paper, we propose a new efficient algorithmic framework for Mumford-Shah regularization of inverse problems in imaging. It is based on a splitting into specific subproblems that can be solved exactly. We derive fast solvers for the subproblems which are key for an efficient overall algorithm. Our method neither requires a priori knowledge of the gray or color levels nor of the shape of the discontinuity set. We demonstrate the wide applicability of the method for different modalities. In particular, we consider the reconstruction from Radon data, inpainting, and deconvolution. Our method can be easily adapted to many further imaging setups. The relevant condition is that the proximal mapping of the data fidelity can be evaluated a within reasonable time. In other words, it can be used whenever classical Tikhonov regularization is possible.
The direct and inverse problems of an air-saturated porous cylinder submitted to acoustic radiation
NASA Astrophysics Data System (ADS)
Ogam, Erick; Depollier, Claude; Fellah, Z. E. A.
2010-09-01
Gas-saturated porous skeleton materials such as geomaterials, polymeric and metallic foams, or biomaterials are fundamental in a diverse range of applications, from structural materials to energy technologies. Most polymeric foams are used for noise control applications and knowledge of the manner in which the energy of sound waves is dissipated with respect to the intrinsic acoustic properties is important for the design of sound packages. Foams are often employed in the audible, low frequency range where modeling and measurement techniques for the recovery of physical parameters responsible for energy loss are still few. Accurate acoustic methods of characterization of porous media are based on the measurement of the transmitted and/or reflected acoustic waves by platelike specimens at ultrasonic frequencies. In this study we develop an acoustic method for the recovery of the material parameters of a rigid-frame, air-saturated polymeric foam cylinder. A dispersion relation for sound wave propagation in the porous medium is derived from the propagation equations and a model solution is sought based on plane-wave decomposition using orthogonal cylindrical functions. The explicit analytical solution equation of the scattered field shows that it is also dependent on the intrinsic acoustic parameters of the porous cylinder, namely, porosity, tortuosity, and flow resistivity (permeability). The inverse problem of the recovery of the flow resistivity and porosity is solved by seeking the minima of the objective functions consisting of the sum of squared residuals of the differences between the experimental and theoretical scattered field data.
Iterative refinement of the minimum norm solution of the bioelectric inverse problem.
Srebro, R
1996-05-01
Functional brain imaging based on the bioelectric scalp field depends on the solution of the inverse problem. The object is to estimate the current distribution in the cortex from a measured noisy scalp potential field. A minimum norm least squares solution is often used for this purpose. Although this approach leads to a unique solution, it represents only one of a set of feasible solutions. In particular, it leads to a solution in which current is found to be generated widely in the cortex. There are other feasible minimum norm solutions for which cortical current is generated only in a restricted region, and it is likely that in many cases, these solutions are of physiological importance. This study presents a method to uncover these solutions. It is based on the idea that a minimum norm solution can be used to define a region of interest, an ellipsoid, within which it is worthwhile to search for another feasible solution. The minimum norm approach is used iteratively and the ellipsoid shrinks. As it shrinks it provides evidence pointing to the location of the active cortical region. The method explicitly recognizes the role of measurement noise in defining feasible solutions. It is tested in a model of the human head that incorporates a realistic cortex in a three-shell sphere. The method improves the localization of cortical activity mimicking physiological processing. PMID:8849467
Rostami, Mahboubeh Rahmati; Wu, Jincheng; Tzanakakis, Emmanuel S
2015-08-20
The cultivation of stem cells as aggregates in scalable bioreactor cultures is an appealing modality for the large-scale manufacturing of stem cell products. Aggregation phenomena are central to such bioprocesses affecting the viability, proliferation and differentiation trajectory of stem cells but a quantitative framework is currently lacking. A population balance equation (PBE) model was used to describe the temporal evolution of the embryonic stem cell (ESC) cluster size distribution by considering collision-induced aggregation and cell proliferation in a stirred-suspension vessel. For ESC cultures at different agitation rates, the aggregation kernel representing the aggregation dynamics was successfully recovered as a solution of the inverse problem. The rate of change of the average aggregate size was greater at the intermediate rate tested suggesting a trade-off between increased collisions and agitation-induced shear. Results from forward simulation with obtained aggregation kernels were in agreement with transient aggregate size data from experiments. We conclude that the framework presented here can complement mechanistic studies offering insights into relevant stem cell clustering processes. More importantly from a process development standpoint, this strategy can be employed in the design and control of bioreactors for the generation of stem cell derivatives for drug screening, tissue engineering and regenerative medicine. PMID:26036699
An analytical approach to estimate the number of small scatterers in 2D inverse scattering problems
NASA Astrophysics Data System (ADS)
Fazli, Roohallah; Nakhkash, Mansor
2012-07-01
This paper presents an analytical method to estimate the location and number of actual small targets in 2D inverse scattering problems. This method is motivated from the exact maximum likelihood estimation of signal parameters in white Gaussian noise for the linear data model. In the first stage, the method uses the MUSIC algorithm to acquire all possible target locations and in the next stage, it employs an analytical formula that works as a spatial filter to determine which target locations are associated to the actual ones. The ability of the method is examined for both the Born and multiple scattering cases and for the cases of well-resolved and non-resolved targets. Many numerical simulations using both the coincident and non-coincident arrays demonstrate that the proposed method can detect the number of actual targets even in the case of very noisy data and when the targets are closely located. Using the experimental microwave data sets, we further show that this method is successful in specifying the number of small inclusions.
On the inverse problem of blade design for centrifugal pumps and fans
NASA Astrophysics Data System (ADS)
Kruyt, N. P.; Westra, R. W.
2014-06-01
The inverse problem of blade design for centrifugal pumps and fans has been studied. The solution to this problem provides the geometry of rotor blades that realize specified performance characteristics, together with the corresponding flow field. Here a three-dimensional solution method is described in which the so-called meridional geometry is fixed and the distribution of the azimuthal angle at the three-dimensional blade surface is determined for blades of infinitesimal thickness. The developed formulation is based on potential-flow theory. Besides the blade impermeability condition at the pressure and suction side of the blades, an additional boundary condition at the blade surface is required in order to fix the unknown blade geometry. For this purpose the mean-swirl distribution is employed. The iterative numerical method is based on a three-dimensional finite element method approach in which the flow equations are solved on the domain determined by the latest estimate of the blade geometry, with the mean-swirl distribution boundary condition at the blade surface being enforced. The blade impermeability boundary condition is then used to find an improved estimate of the blade geometry. The robustness of the method is increased by specific techniques, such as spanwise-coupled solution of the discretized impermeability condition and the use of under-relaxation in adjusting the estimates of the blade geometry. Various examples are shown that demonstrate the effectiveness and robustness of the method in finding a solution for the blade geometry of different types of centrifugal pumps and fans. The influence of the employed mean-swirl distribution on the performance characteristics is also investigated.
NASA Astrophysics Data System (ADS)
Giudici, M.; Baratelli, F.; Comunian, A.; Vassena, C.; Cattaneo, L.
2014-10-01
Numerical modelling of the dynamic evolution of ice sheets and glaciers requires the solution of discrete equations which are based on physical principles (e.g. conservation of mass, linear momentum and energy) and phenomenological constitutive laws (e.g. Glen's and Fourier's laws). These equations must be accompanied by information on the forcing term and by initial and boundary conditions (IBCs) on ice velocity, stress and temperature; on the other hand the constitutive laws involve many physical parameters, some of which depend on the ice thermodynamical state. The proper forecast of the dynamics of ice sheets and glaciers requires a precise knowledge of several quantities which appear in the IBCs, in the forcing terms and in the phenomenological laws. As these quantities cannot be easily measured at the study scale in the field, they are often obtained through model calibration by solving an inverse problem (IP). The objective of this paper is to provide a thorough and rigorous conceptual framework for IPs in cryospheric studies and in particular: to clarify the role of experimental and monitoring data to determine the calibration targets and the values of the parameters that can be considered to be fixed; to define and characterise identifiability, a property related to the solution to the forward problem; to study well-posedness in a correct way, without confusing instability with ill-conditioning or with the properties of the method applied to compute a solution; to cast sensitivity analysis in a general framework and to differentiate between the computation of local sensitivity indicators with a one-at-a-time approach and first-order sensitivity indicators that consider the whole possible variability of the model parameters. The conceptual framework and the relevant properties are illustrated by means of a simple numerical example of isothermal ice flow, based on the shallow-ice approximation.
Inverse Tasks In The Tsunami Problem: Nonlinear Regression With Inaccurate Input Data
NASA Astrophysics Data System (ADS)
Lavrentiev, M.; Shchemel, A.; Simonov, K.
problem can be formally propounded this way: A distribution of various combinations of observed values should be estimated. Totality of the combinations is represented by the set of variables. The results of observations determine excerption of outputs. In the scope of the propounded problem continuous (along with its derivations) homomorphic reflec- tion of the space of hidden parameters to the space of observed parameters should be found. It allows to reconstruct lack information of the inputs when the number of the 1 inputs is not less than the number of hidden parameters and to estimate the distribution if information for synonymous prediction of unknown inputs is not sufficient. The following approach to build approximation based on the excerption is suggested: the excerption is supplemented with the hidden parameters, which are distributed uni- formly in a multidimensional limited space. Then one should find correspondence of model and observed outputs. Therefore the correspondence will provide that the best approximation is the most accurate. In the odd iterations dependence between hid- den inputs and outputs is being optimized (like the conventional problem is solved). Correspondence between tasks is changing in the case when the error is reducing and distribution of inputs remains intact. Therefore, a special transform is applied to reduce error at every iteration. If the mea- sure of distribution is constant, then the condition of transformations is simplified. Such transforms are named "canonical" or "volume invariant transforms" and, there- fore, are well known. This approach is suggested for solving main inverse task of the tsunami problem. Basing on registered tsunami in seaside and shelf to estimate parameters of tsunami's hearth. 2
NASA Astrophysics Data System (ADS)
Liu, Yun; Zhang, Yin
2016-06-01
The mass sensing superiority of a micro/nanomechanical resonator sensor over conventional mass spectrometry has been, or at least, is being firmly established. Because the sensing mechanism of a mechanical resonator sensor is the shifts of resonant frequencies, how to link the shifts of resonant frequencies with the material properties of an analyte formulates an inverse problem. Besides the analyte/adsorbate mass, many other factors such as position and axial force can also cause the shifts of resonant frequencies. The in-situ measurement of the adsorbate position and axial force is extremely difficult if not impossible, especially when an adsorbate is as small as a molecule or an atom. Extra instruments are also required. In this study, an inverse problem of using three resonant frequencies to determine the mass, position and axial force is formulated and solved. The accuracy of the inverse problem solving method is demonstrated and how the method can be used in the real application of a nanomechanical resonator is also discussed. Solving the inverse problem is helpful to the development and application of mechanical resonator sensor on two things: reducing extra experimental equipments and achieving better mass sensing by considering more factors.
NASA Astrophysics Data System (ADS)
Nassar, Mohamed K.; Ginn, Timothy R.
2014-08-01
We investigate the effect of computational error on the inversion of a density-dependent flow and transport model, using SEAWAT and UCODE-2005 in an inverse identification of hydraulic conductivity and dispersivity using head and concentration data from a 2-D laboratory experiment. We investigated inversions using three different solution schemes including variation of number of particles and time step length, in terms of the three aspects: the shape and smoothness of the objective function surface, the consequent impacts to the optimization, and the resulting Pareto analyses. This study demonstrates that the inversion is very sensitive to the choice of the forward model solution scheme. In particular, standard finite difference methods provide the smoothest objective function surface; however, this is obtained at the cost of numerical artifacts that can lead to erroneous warping of the objective function surface. Total variation diminishing (TVD) schemes limit these impacts at the cost of more computation time, while the hybrid method of characteristics (HMOC) approach with increased particle numbers and/or reduced time step gives both smoothed and accurate objective function surface. Use of the most accurate methods (TVD and HMOC) did lead to successful inversion of the two parameters; however, with distinct results for Pareto analyses. These results illuminate the sensitivity of the inversion to a number of aspects of the forward solution of the density-driven flow problem and reveal that parameter values may result that are erroneous but that counteract numerical errors in the solution.
Magnetic field topology of τ Scorpii. The uniqueness problem of Stokes V ZDI inversions
NASA Astrophysics Data System (ADS)
Kochukhov, O.; Wade, G. A.
2016-02-01
Context. The early B-type star τ Sco exhibits an unusually complex, relatively weak surface magnetic field. Its topology was previously studied with the Zeeman Doppler imaging (ZDI) modelling of high-resolution circular polarisation (Stokes V) observations. Aims: Here we assess the robustness of the Stokes V ZDI reconstruction of the magnetic field geometry of τ Sco and explore the consequences of using different parameterisations of the surface magnetic maps. Methods: This analysis is based on the archival ESPaDOnS high-resolution Stokes V observations and employs an independent ZDI magnetic inversion code. Results: We succeeded in reproducing previously published magnetic field maps of τ Sco using both general harmonic expansion and a direct, pixel-based representation of the magnetic field. These maps suggest that the field topology of τ Sco is comprised of comparable contributions of the poloidal and toroidal magnetic components. At the same time, we also found that available Stokes V observations can be successfully fitted with restricted harmonic expansions, by either neglecting the toroidal field altogether, or linking the radial and horizontal components of the poloidal field as required by the widely used potential field extrapolation technique. These alternative modelling approaches lead to a stronger and topologically more complex surface field structure. The field distributions, which were recovered with different ZDI options, differ significantly and yield indistinguishable Stokes V profiles but different linear polarisation (Stokes Q and U) signatures. Conclusions: Our investigation underscores the well-known problem of non-uniqueness of the Stokes V ZDI inversions. For the magnetic stars with properties similar to τ Sco (relatively complex field, slow rotation) the outcome of magnetic reconstruction strongly depends on the adopted field parameterisation, rendering photospheric magnetic mapping and determination of the extended magnetospheric
NASA Astrophysics Data System (ADS)
Huang, C.-H.; Hsu, G.-C.; Jang, J.-Y.
A nonlinear inverse problem utilizing the Conjugate Gradient Method (CGM) of minimization is used successfully to estimate the temporally and circumferentially varying thermal contact conductance of a plate finned-tube heat exchanger by reading the simulated transient temperature measurement data from the thermocouples located on the plate. The thermal properties of the fin and tube are assumed to be functions of temperature, and this makes the problem nonlinear. It is assumed that no prior information is available on the functional form of the unknown thermal contact conductance in the present study, thus, it is classified as the function estimation in the inverse calculation. The accuracy of the inverse analysis is examined by using the simulated temperature measurements. Finally the inverse solutions with and without the consideration of temperature-dependent thermal properties are compared. Results show that when the nonlinear inverse calculations are performed an excellent estimation on the thermal contact conductance can be obtained with any arbitrary initial guesses within a couple of minute's CPU time on a HP-730 workstation.
Pesin, Yakov B.; Niu, Xun; Latash, Mark L.
2010-01-01
We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms. PMID:19902213
Bakhos, Tania; Saibaba, Arvind K.; Kitanidis, Peter K.
2015-10-15
We consider the problem of estimating parameters in large-scale weakly nonlinear inverse problems for which the underlying governing equations is a linear, time-dependent, parabolic partial differential equation. A major challenge in solving these inverse problems using Newton-type methods is the computational cost associated with solving the forward problem and with repeated construction of the Jacobian, which represents the sensitivity of the measurements to the unknown parameters. Forming the Jacobian can be prohibitively expensive because it requires repeated solutions of the forward and adjoint time-dependent parabolic partial differential equations corresponding to multiple sources and receivers. We propose an efficient method based on a Laplace transform-based exponential time integrator combined with a flexible Krylov subspace approach to solve the resulting shifted systems of equations efficiently. Our proposed solver speeds up the computation of the forward and adjoint problems, thus yielding significant speedup in total inversion time. We consider an application from Transient Hydraulic Tomography (THT), which is an imaging technique to estimate hydraulic parameters related to the subsurface from pressure measurements obtained by a series of pumping tests. The algorithms discussed are applied to a synthetic example taken from THT to demonstrate the resulting computational gains of this proposed method.
NASA Astrophysics Data System (ADS)
Nassar, M.; Ginn, T.
2012-12-01
The purpose of this study is to investigate the effect of the computational error on the solution of the inverse problem connecting with density-dependent flow problem. This effect will be addressed by evaluating the uniqueness of the inverse via monitoring objective function surface behavior in two dimensions parameter space, hydraulic conductivity and longitudinal dispersivity. In addition, the Pareto surface will be generated to evaluate the trade-offs between two calibration objectives based on head and concentration measurement errors. This is conducted by changing the aspects of forward model solution scheme, Eulerian and Lagrangian methods with associated variables. The data used for this study is based on the lab study of Nassar et al (2008). The seepage tank is essentially 2D (in an x-z vertical plane) with relatively homogenous coarse sand media with assigned flux in the upstream and constant head or assigned flux boundary condition at the downstream. The forward model solution is conducted with SEAWAT and it is utilized jointly with the inverse code UCODE-2005. This study demonstrates that the choice of the different numerical scheme with associated aspects of the forward problem is a vital step in the solution of the inverse problem in indirect manner. The method of characteristics gives good results by increasing the initial particles numbers and/ or reducing the time step. The advantage of using more particles concept over decreasing the time step is in smoothing the objective function surface that enable the gradient based search technique works in efficient way. Also, the selected points on the Pareto surface is collapsed to two points on the objective function space. Most likely they are not collapsed to a single point in objective function space with one best parameter set because the problem is advection dominating problem.
Albocher, U.; Barbone, P.E.; Richards, M.S.; Oberai, A.A.; Harari, I.
2014-01-01
We apply the adjoint weighted equation method (AWE) to the direct solution of inverse problems of incompressible plane strain elasticity. We show that based on untreated noisy displacements, the reconstruction of the shear modulus can be very poor. We link this poor performance to loss of coercivity of the weak form when treating problems with discontinuous coefficients. We demonstrate that by smoothing the displacements and appending a regularization term to the AWE formulation, a dramatic improvement in the reconstruction can be achieved. With these improvements, the advantages of the AWE method as a direct solution approach can be extended to a wider range of problems. PMID:25383085
Piskozub, J.
1994-12-31
The multifrequency lidar inverse problem discussed consists of calculating the size distribution of sol particles from backscattered lidar data. Sea-water (marine) aerosol is particularly well suited for this kind of study as its scattering characteristics can be accurately represented by Mie theory as its particles are almost spherical and their complex index of refraction is well known. Here, a solution of the inverse problem concerning finding aerosol size distribution for a multifrequency lidar system working on a small number of wavelengths is proposed. The solution involves a best-fit method of finding parameters in a pre-set formula of particle size distribution. A comparison of results calculated with the algorithm from experimental lidar profiles with PMS data collected in Baltic Sea coastal zone is given.
Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states
NASA Astrophysics Data System (ADS)
Decelle, Aurélien; Ricci-Tersenghi, Federico
2016-07-01
In this work we explain how to properly use mean-field methods to solve the inverse Ising problem when the phase space is clustered, that is, many states are present. The clustering of the phase space can occur for many reasons, e.g., when a system undergoes a phase transition, but also when data are collected in different regimes (e.g., quiescent and spiking regimes in neural networks). Mean-field methods for the inverse Ising problem are typically used without taking into account the eventual clustered structure of the input configurations and may lead to very poor inference (e.g., in the low-temperature phase of the Curie-Weiss model). In this work we explain how to modify mean-field approaches when the phase space is clustered and we illustrate the effectiveness of our method on different clustered structures (low-temperature phases of Curie-Weiss and Hopfield models).
Solving the inverse Ising problem by mean-field methods in a clustered phase space with many states.
Decelle, Aurélien; Ricci-Tersenghi, Federico
2016-07-01
In this work we explain how to properly use mean-field methods to solve the inverse Ising problem when the phase space is clustered, that is, many states are present. The clustering of the phase space can occur for many reasons, e.g., when a system undergoes a phase transition, but also when data are collected in different regimes (e.g., quiescent and spiking regimes in neural networks). Mean-field methods for the inverse Ising problem are typically used without taking into account the eventual clustered structure of the input configurations and may lead to very poor inference (e.g., in the low-temperature phase of the Curie-Weiss model). In this work we explain how to modify mean-field approaches when the phase space is clustered and we illustrate the effectiveness of our method on different clustered structures (low-temperature phases of Curie-Weiss and Hopfield models). PMID:27575082
NASA Astrophysics Data System (ADS)
Akhmedzhanov, I. M.; Baranov, D. V.; Zolotov, E. M.
2016-06-01
The existence, uniqueness, and stability of the inverse problem solution for a scanning differential heterodyne microscope as applied to rectangular plasmonic waveguides have been analyzed. The consideration is based on an algorithm using a trial-and-error method that we proposed previously to characterize plasmonic waveguides with a triangular profile. The error of the inverse problem (IP) solution is calculated as dependent on the initial data and with allowance for their errors. Instability domains are found for the IP solution, where the solution error sharply increases. It is shown that the instability domains can be eliminated and the accuracy of the IP solution can be significantly improved in the entire range of initial data by taking initial data in the form of two phase responses of the microscope at different wavelengths.
NASA Astrophysics Data System (ADS)
Barenboim, Gabriela; Park, Wan-Il
2016-08-01
We investigate the gravitational wave background from a first order phase transition in a matter-dominated universe, and show that it has a unique feature from which important information about the properties of the phase transition and thermal history of the universe can be easily extracted. Also, we discuss the inverse problem of such a gravitational wave background in view of the degeneracy among macroscopic parameters governing the signal.
NASA Astrophysics Data System (ADS)
Cui, Tiangang; Marzouk, Youssef; Willcox, Karen
2016-06-01
Two major bottlenecks to the solution of large-scale Bayesian inverse problems are the scaling of posterior sampling algorithms to high-dimensional parameter spaces and the computational cost of forward model evaluations. Yet incomplete or noisy data, the state variation and parameter dependence of the forward model, and correlations in the prior collectively provide useful structure that can be exploited for dimension reduction in this setting-both in the parameter space of the inverse problem and in the state space of the forward model. To this end, we show how to jointly construct low-dimensional subspaces of the parameter space and the state space in order to accelerate the Bayesian solution of the inverse problem. As a byproduct of state dimension reduction, we also show how to identify low-dimensional subspaces of the data in problems with high-dimensional observations. These subspaces enable approximation of the posterior as a product of two factors: (i) a projection of the posterior onto a low-dimensional parameter subspace, wherein the original likelihood is replaced by an approximation involving a reduced model; and (ii) the marginal prior distribution on the high-dimensional complement of the parameter subspace. We present and compare several strategies for constructing these subspaces using only a limited number of forward and adjoint model simulations. The resulting posterior approximations can rapidly be characterized using standard sampling techniques, e.g., Markov chain Monte Carlo. Two numerical examples demonstrate the accuracy and efficiency of our approach: inversion of an integral equation in atmospheric remote sensing, where the data dimension is very high; and the inference of a heterogeneous transmissivity field in a groundwater system, which involves a partial differential equation forward model with high dimensional state and parameters.
NASA Astrophysics Data System (ADS)
Jahn, Kornél; Bokor, Nándor
2013-02-01
A technique using vector Slepian harmonics and vector Slepian multipole fields is presented for a general treatment of the inverse problem of high numerical aperture focusing. A prescribed intensity distribution or electric field distribution in the focal volume is approximated using numerical optimization and the corresponding illuminating field at the entrance pupil is constructed. Three examples from the recent literature are chosen to illustrate the method.
NASA Astrophysics Data System (ADS)
Pavel'Ev, A. G.
1987-07-01
Kotel'nikov's theorem is used to determine the frequency range in which the inverse refraction problem is well-posed. Inside this range, small errors in the solution correspond to small experimental errors. Outside this range, regularization should be carried out with a differential technique involving the determination of the difference between the exact solution and the experimentally measured refraction relationship. The proposed approach is confirmed through an analysis of bistatic radar sounding data for Venus.
NASA Astrophysics Data System (ADS)
Hong, Soonsung
A framework for an inverse analysis of the crack-tip cohesive zone model has been developed to measure the cohesive zone laws by analytical, experimental and numerical approaches. An analytical solution method of the inverse problem is developed to extract cohesive-zone laws from elastic far-fields surrounding a crack-tip cohesive zone. A general form of cohesive-crack-tip fields is obtained and used for eigenfunction expansions of the plane elastic field in a complex variable representation. The closing tractions and the separation-gradients at the cohesive zone are expressed in terms of orthogonal polynomial series expansions of the general-form complex functions. The coefficients of the eigenfunctions in J-orthogonal representation are extracted directly, using interaction J-integrals at far field between the physical field and auxiliary fields. The results from numerical experiments suggest that the proposed algorithms are well suited for extracting cohesive-zone laws from the far-field data. An experimental measurement technique called the Electronic Speckle Pattern Interferometry has been applied to measure whole-field crack-tip displacement near fracture process zones. A 4-beam laser speckle interferometer has been constructed to measure 2-dimensional displacement field around a crack tip in glassy polymers. The experimentally measured deformation fields around a crack-tip are used later as input data for the inverse analysis of the crack-tip cohesive zone problem. A numerical noise reduction algorithm, called Equilibrium Smoothing Method, is developed to extract smooth equilibrium fields from the experimentally measured displacement fields. The obtained smooth displacement field is used in inverse analysis of the crack problem. Two nonlinear fracture processes in glassy polymers have been investigated by the framework for the inverse problem. The environmental crazing in PMMA under methanol environment and the nonlinear fracture process in the rubber
NASA Astrophysics Data System (ADS)
Giudici, Mauro; Baratelli, Fulvia; Vassena, Chiara; Cattaneo, Laura
2014-05-01
Numerical modelling of the dynamic evolution of ice sheets and glaciers requires the solution of discrete equations which are based on physical principles (e.g. conservation of mass, linear momentum and energy) and phenomenological constitutive laws (e.g. Glen's and Fourier's laws). These equations must be accompanied by information on the forcing term and by initial and boundary conditions (IBC) on ice velocity, stress and temperature; on the other hand the constitutive laws involves many physical parameters, which possibly depend on the ice thermodynamical state. The proper forecast of the dynamics of ice sheets and glaciers (forward problem, FP) requires a precise knowledge of several quantities which appear in the IBCs, in the forcing terms and in the phenomenological laws and which cannot be easily measured at the study scale in the field. Therefore these quantities can be obtained through model calibration, i.e. by the solution of an inverse problem (IP). Roughly speaking, the IP aims at finding the optimal values of the model parameters that yield the best agreement of the model output with the field observations and data. The practical application of IPs is usually formulated as a generalised least squares approach, which can be cast in the framework of Bayesian inference. IPs are well developed in several areas of science and geophysics and several applications were proposed also in glaciology. The objective of this paper is to provide a further step towards a thorough and rigorous theoretical framework in cryospheric studies. Although the IP is often claimed to be ill-posed, this is rigorously true for continuous domain models, whereas for numerical models, which require the solution of algebraic equations, the properties of the IP must be analysed with more care. First of all, it is necessary to clarify the role of experimental and monitoring data to determine the calibration targets and the values of the parameters that can be considered to be fixed
NASA Astrophysics Data System (ADS)
Dorn, O.; Lesselier, D.
2010-07-01
Inverse problems in electromagnetics have a long history and have stimulated exciting research over many decades. New applications and solution methods are still emerging, providing a rich source of challenging topics for further investigation. The purpose of this special issue is to combine descriptions of several such developments that are expected to have the potential to fundamentally fuel new research, and to provide an overview of novel methods and applications for electromagnetic inverse problems. There have been several special sections published in Inverse Problems over the last decade addressing fully, or partly, electromagnetic inverse problems. Examples are: Electromagnetic imaging and inversion of the Earth's subsurface (Guest Editors: D Lesselier and T Habashy) October 2000 Testing inversion algorithms against experimental data (Guest Editors: K Belkebir and M Saillard) December 2001 Electromagnetic and ultrasonic nondestructive evaluation (Guest Editors: D Lesselier and J Bowler) December 2002 Electromagnetic characterization of buried obstacles (Guest Editors: D Lesselier and W C Chew) December 2004 Testing inversion algorithms against experimental data: inhomogeneous targets (Guest Editors: K Belkebir and M Saillard) December 2005 Testing inversion algorithms against experimental data: 3D targets (Guest Editors: A Litman and L Crocco) February 2009 In a certain sense, the current issue can be understood as a continuation of this series of special sections on electromagnetic inverse problems. On the other hand, its focus is intended to be more general than previous ones. Instead of trying to cover a well-defined, somewhat specialized research topic as completely as possible, this issue aims to show the broad range of techniques and applications that are relevant to electromagnetic imaging nowadays, which may serve as a source of inspiration and encouragement for all those entering this active and rapidly developing research area. Also, the
On parameterization of the inverse problem for estimating aquifer properties using tracer data
NASA Astrophysics Data System (ADS)
Kowalsky, M. B.; Finsterle, S.; Williams, K. H.; Murray, C.; Commer, M.; Newcomer, D.; Englert, A.; Steefel, C. I.; Hubbard, S. S.
2012-06-01
In developing a reliable approach for inferring hydrological properties through inverse modeling of tracer data, decisions made on how to parameterize heterogeneity (i.e., how to represent a heterogeneous distribution using a limited number of parameters that are amenable to estimation) are of paramount importance, as errors in the model structure are partly compensated for by estimating biased property values during the inversion. These biased estimates, while potentially providing an improved fit to the calibration data, may lead to wrong interpretations and conclusions and reduce the ability of the model to make reliable predictions. We consider the estimation of spatial variations in permeability and several other parameters through inverse modeling of tracer data, specifically synthetic and actual field data associated with the 2007 Winchester experiment from the Department of Energy Rifle site. Characterization is challenging due to the real-world complexities associated with field experiments in such a dynamic groundwater system. Our aim is to highlight and quantify the impact on inversion results of various decisions related to parameterization, such as the positioning of pilot points in a geostatistical parameterization; the handling of up-gradient regions; the inclusion of zonal information derived from geophysical data or core logs; extension from 2-D to 3-D; assumptions regarding the gradient direction, porosity, and the semivariogram function; and deteriorating experimental conditions. This work adds to the relatively limited number of studies that offer guidance on the use of pilot points in complex real-world experiments involving tracer data (as opposed to hydraulic head data).
PREFACE: 6th International Conference on Inverse Problems in Engineering: Theory and Practice
NASA Astrophysics Data System (ADS)
Bonnet, Marc
2008-07-01
The 6th International Conference on Inverse Problems in Engineering: Theory and Practice (ICIPE 2008) belongs to a successful series of conferences held up to now following a three-year cycle. Previous conferences took place in Palm Coast, Florida, USA (1993), Le Croisic, France (1996), Port Ludlow, Washington, USA (1999), Angra dos Reis, Brazil (2002), and Cambridge, UK (2005). The conference has its roots on the informal seminars organized by Professor J V Beck at Michigan State University, which were initiated in 1987. The organization of this Conference, which took place in Dourdan (Paris) France, 15-19 June 2008, was made possible through a joint effort by four research departments from four different universities: LEMTA (Laboratoire de Mécanique Théorique et Appliquée, Nancy-Université) LMS (Laboratoire de Mécanique des Solides, Ecole Polytechnique, Paris) LMAC (Laboratoire de Mathématiques Appliquées, UTC Compiègne) LTN (Laboratoire de Thermocinétique, Université de Nantes) It received support from three organizations: SFT (Société Française de Thermique: French Heat Transfer Association) ACSM (Association Calcul de Structures et Simulation : Computational Structural Mechanics Association) GdR Ondes - CNRS (`Waves' Network, French National Center for Scientific Research) The objective of the conference was to provide the opportunity for interaction and cross-fertilization between designers of inverse methods and practitioners. The delegates came from very different fields, such as applied mathematics, heat transfer, solid mechanics, tomography.... Consequently the sessions were organised along mostly methodological topics in order to facilitate interaction among participants who might not meet otherwise. The present proceedings, published in the Journal of Physics: Conference Series, gathers the four plenary invited lectures and the full-length versions of 103 presentations. The latter have been reviewed by the scientific committee (see
Local sensitivity analysis for inverse problems solved by singular value decomposition
Hill, M.C.; Nolan, B.T.
2010-01-01
Local sensitivity analysis provides computationally frugal ways to evaluate models commonly used for resource management, risk assessment, and so on. This includes diagnosing inverse model convergence problems caused by parameter insensitivity and(or) parameter interdependence (correlation), understanding what aspects of the model and data contribute to measures of uncertainty, and identifying new data likely to reduce model uncertainty. Here, we consider sensitivity statistics relevant to models in which the process model parameters are transformed using singular value decomposition (SVD) to create SVD parameters for model calibration. The statistics considered include the PEST identifiability statistic, and combined use of the process-model parameter statistics composite scaled sensitivities and parameter correlation coefficients (CSS and PCC). The statistics are complimentary in that the identifiability statistic integrates the effects of parameter sensitivity and interdependence, while CSS and PCC provide individual measures of sensitivity and interdependence. PCC quantifies correlations between pairs or larger sets of parameters; when a set of parameters is intercorrelated, the absolute value of PCC is close to 1.00 for all pairs in the set. The number of singular vectors to include in the calculation of the identifiability statistic is somewhat subjective and influences the statistic. To demonstrate the statistics, we use the USDA’s Root Zone Water Quality Model to simulate nitrogen fate and transport in the unsaturated zone of the Merced River Basin, CA. There are 16 log-transformed process-model parameters, including water content at field capacity (WFC) and bulk density (BD) for each of five soil layers. Calibration data consisted of 1,670 observations comprising soil moisture, soil water tension, aqueous nitrate and bromide concentrations, soil nitrate concentration, and organic matter content. All 16 of the SVD parameters could be estimated by
HT2DINV: A 2D forward and inverse code for steady-state and transient hydraulic tomography problems
NASA Astrophysics Data System (ADS)
Soueid Ahmed, A.; Jardani, A.; Revil, A.; Dupont, J. P.
2015-12-01
Hydraulic tomography is a technique used to characterize the spatial heterogeneities of storativity and transmissivity fields. The responses of an aquifer to a source of hydraulic stimulations are used to recover the features of the estimated fields using inverse techniques. We developed a 2D free source Matlab package for performing hydraulic tomography analysis in steady state and transient regimes. The package uses the finite elements method to solve the ground water flow equation for simple or complex geometries accounting for the anisotropy of the material properties. The inverse problem is based on implementing the geostatistical quasi-linear approach of Kitanidis combined with the adjoint-state method to compute the required sensitivity matrices. For undetermined inverse problems, the adjoint-state method provides a faster and more accurate approach for the evaluation of sensitivity matrices compared with the finite differences method. Our methodology is organized in a way that permits the end-user to activate parallel computing in order to reduce the computational burden. Three case studies are investigated demonstrating the robustness and efficiency of our approach for inverting hydraulic parameters.
NASA Technical Reports Server (NTRS)
Sidi, Avram; Pennline, James A.
1999-01-01
In this paper we are concerned with high-accuracy quadrature method solutions of nonlinear Fredholm integral equations of the form y(x) = r(x) + definite integral of g(x, t)F(t,y(t))dt with limits between 0 and 1,0 less than or equal to x les than or equal to 1, where the kernel function g(x,t) is continuous, but its partial derivatives have finite jump discontinuities across x = t. Such integral equations arise, e.g., when one applied Green's function techniques to nonlinear two-point boundary value problems of the form y "(x) =f(x,y(x)), 0 less than or equal to x less than or equal to 1, with y(0) = y(sub 0) and y(l) = y(sub l), or other linear boundary conditions. A quadrature method that is especially suitable and that has been employed for such equations is one based on the trepezoidal rule that has a low accuracy. By analyzing the corresponding Euler-Maclaurin expansion, we derive suitable correction terms that we add to the trapezoidal rule, thus obtaining new numerical quadrature formulas of arbitrarily high accuracy that we also use in defining quadrature methods for the integral equations above. We prove an existence and uniqueness theorem for the quadrature method solutions, and show that their accuracy is the same as that of the underlying quadrature formula. The solution of the nonlinear systems resulting from the quadrature methods is achieved through successive approximations whose convergence is also proved. The results are demonstrated with numerical examples.
NASA Technical Reports Server (NTRS)
Sidi, Avram; Pennline, James A.
1999-01-01
In this paper we are concerned with high-accuracy quadrature method solutions of nonlinear Fredholm integral equations of the form y(x) = r(x) + integral(0 to 1) g(x,t) F(t, y(t)) dt, 0 less than or equal to x less than or equal to 1, where the kernel function g(x,t) is continuous, but its partial derivatives have finite jump discontinuities across x = t. Such integrals equations arise, e.g., when one applies Green's function techniques to nonlinear two-point boundary value problems of the form U''(x) = f(x,y(x)), 0 less than or equal to x less than or equal to 1, with y(0) = y(sub 0) and g(l) = y(sub 1), or other linear boundary conditions. A quadrature method that is especially suitable and that has been employed for such equations is one based on the trapezoidal rule that has a low accuracy. By analyzing the corresponding Euler-Maclaurin expansion, we derive suitable correction terms that we add to the trapezoidal thus obtaining new numerical quadrature formulas of arbitrarily high accuracy that we also use in defining quadrature methods for the integral equations above. We prove an existence and uniqueness theorem for the quadrature method solutions, and show that their accuracy is the same as that of the underlying quadrature formula. The solution of the nonlinear systems resulting from the quadrature methods is achieved through successive approximations whose convergence is also proved. The results are demonstrated with numerical examples.
Aldemir, Tunc
2011-01-01
Determining the components of a radioactive source/shield system using the system's radiation signature, a type of inverse transport problem, is one of great importance in homeland security, material safeguards, and waste management. Here, the Levenberg-Marquardt (or simply 'Marquardt') method, a standard gradient-based optimization technique, is applied to the inverse transport problems of interface location identification, shield material identification, source composition identification, and material mass density identification (both separately and combined) in multilayered radioactive source/shield systems. One-dimensional spherical problems using leakage measurements of neutron-induced gamma-ray lines and two-dimensional cylindrical problems using flux measurements of uncollided passive gamma-ray lines are considered. Gradients are calculated using an adjoint-based differentiation technique that is more efficient than difference formulas. The Marquardt method is iterative and directly estimates unknown interface locations, source isotope weight fractions, and material mass densities, while the unknown shield material is identified by estimating its macroscopic gamma-ray cross sections. Numerical test cases illustrate the utility of the Marquardt method using both simulated data that are perfectly consistent with the optimization process and realistic data simulated by Monte Carlo.
NASA Astrophysics Data System (ADS)
Nielsen, Bjørn Fredrik; Lysaker, Marius; Tveito, Aslak
2007-01-01
The electrical activity in the heart is modeled by a complex, nonlinear, fully coupled system of differential equations. Several scientists have studied how this model, referred to as the bidomain model, can be modified to incorporate the effect of heart infarctions on simulated ECG (electrocardiogram) recordings. We are concerned with the associated inverse problem; how can we use ECG recordings and mathematical models to identify the position, size and shape of heart infarctions? Due to the extreme CPU efforts needed to solve the bidomain equations, this model, in its full complexity, is not well-suited for this kind of problems. In this paper we show how biological knowledge about the resting potential in the heart and level set techniques can be combined to derive a suitable stationary model, expressed in terms of an elliptic PDE, for such applications. This approach leads to a nonlinear ill-posed minimization problem, which we propose to regularize and solve with a simple iterative scheme. Finally, our theoretical findings are illuminated through a series of computer simulations for an experimental setup involving a realistic heart in torso geometry. More specifically, experiments with synthetic ECG recordings, produced by solving the bidomain model, indicate that our method manages to identify the physical characteristics of the ischemic region(s) in the heart. Furthermore, the ill-posed nature of this inverse problem is explored, i.e. several quantitative issues of our scheme are explored.
PREFACE: 6th International Conference on Inverse Problems in Engineering: Theory and Practice
NASA Astrophysics Data System (ADS)
Bonnet, Marc
2008-07-01
The 6th International Conference on Inverse Problems in Engineering: Theory and Practice (ICIPE 2008) belongs to a successful series of conferences held up to now following a three-year cycle. Previous conferences took place in Palm Coast, Florida, USA (1993), Le Croisic, France (1996), Port Ludlow, Washington, USA (1999), Angra dos Reis, Brazil (2002), and Cambridge, UK (2005). The conference has its roots on the informal seminars organized by Professor J V Beck at Michigan State University, which were initiated in 1987. The organization of this Conference, which took place in Dourdan (Paris) France, 15-19 June 2008, was made possible through a joint effort by four research departments from four different universities: LEMTA (Laboratoire de Mécanique Théorique et Appliquée, Nancy-Université) LMS (Laboratoire de Mécanique des Solides, Ecole Polytechnique, Paris) LMAC (Laboratoire de Mathématiques Appliquées, UTC Compiègne) LTN (Laboratoire de Thermocinétique, Université de Nantes) It received support from three organizations: SFT (Société Française de Thermique: French Heat Transfer Association) ACSM (Association Calcul de Structures et Simulation : Computational Structural Mechanics Association) GdR Ondes - CNRS (`Waves' Network, French National Center for Scientific Research) The objective of the conference was to provide the opportunity for interaction and cross-fertilization between designers of inverse methods and practitioners. The delegates came from very different fields, such as applied mathematics, heat transfer, solid mechanics, tomography.... Consequently the sessions were organised along mostly methodological topics in order to facilitate interaction among participants who might not meet otherwise. The present proceedings, published in the Journal of Physics: Conference Series, gathers the four plenary invited lectures and the full-length versions of 103 presentations. The latter have been reviewed by the scientific committee (see
Not Just Hats Anymore: Binomial Inversion and the Problem of Multiple Coincidences
ERIC Educational Resources Information Center
Hathout, Leith
2007-01-01
The well-known "hats" problem, in which a number of people enter a restaurant and check their hats, and then receive them back at random, is often used to illustrate the concept of derangements, that is, permutations with no fixed points. In this paper, the problem is extended to multiple items of clothing, and a general solution to the problem of…
NASA Technical Reports Server (NTRS)
Devasia, Santosh; Bayo, Eduardo
1993-01-01
This paper addresses the problem of inverse dynamics for articulated flexible structures with both lumped and distributed actuators. This problem arises, for example, in the combined vibration minimization and trajectory control of space robots and structures. A new inverse dynamics scheme for computing the nominal lumped and distributed inputs for tracking a prescribed trajectory is given.
NASA Astrophysics Data System (ADS)
Grayver, Alexander V.; Kuvshinov, Alexey V.
2016-05-01
This paper presents a methodology to sample equivalence domain (ED) in nonlinear partial differential equation (PDE)-constrained inverse problems. For this purpose, we first applied state-of-the-art stochastic optimization algorithm called Covariance Matrix Adaptation Evolution Strategy (CMAES) to identify low-misfit regions of the model space. These regions were then randomly sampled to create an ensemble of equivalent models and quantify uncertainty. CMAES is aimed at exploring model space globally and is robust on very ill-conditioned problems. We show that the number of iterations required to converge grows at a moderate rate with respect to number of unknowns and the algorithm is embarrassingly parallel. We formulated the problem by using the generalized Gaussian distribution. This enabled us to seamlessly use arbitrary norms for residual and regularization terms. We show that various regularization norms facilitate studying different classes of equivalent solutions. We further show how performance of the standard Metropolis-Hastings Markov chain Monte Carlo algorithm can be substantially improved by using information CMAES provides. This methodology was tested by using individual and joint inversions of magneotelluric, controlled-source electromagnetic (EM) and global EM induction data.
NASA Astrophysics Data System (ADS)
Grayver, Alexander V.; Kuvshinov, Alexey V.
2016-02-01
This paper presents a methodology to sample equivalence domain (ED) in non-linear PDE-constrained inverse problems. For this purpose, we first applied state-of-the-art stochastic optimization algorithm called Covariance Matrix Adaptation Evolution Strategy (CMAES) to identify low misfit regions of the model space. These regions were then randomly sampled to create an ensemble of equivalent models and quantify uncertainty. CMAES is aimed at exploring model space globally and is robust on very ill-conditioned problems. We show that the number of iterations required to converge grows at a moderate rate with respect to number of unknowns and the algorithm is embarrassingly parallel. We formulated the problem by using the generalized Gaussian distribution. This enabled us to seamlessly use arbitrary norms for residual and regularization terms. We show that various regularization norms facilitate studying different classes of equivalent solutions. We further show how performance of the standard Metropolis-Hastings Markov chain Monte Carlo (MCMC) algorithm can be substantially improved by using information CMAES provides. This methodology was tested by using individual and joint inversions of Magneotelluric, Controlled-source Electromagnetic (EM) and Global EM induction data.
Chemyakin, Eduard; Burton, Sharon; Kolgotin, Alexei; Müller, Detlef; Hostetler, Chris; Ferrare, Richard
2016-03-20
We present an investigation of some important mathematical and numerical features related to the retrieval of microphysical parameters [complex refractive index, single-scattering albedo, effective radius, total number, surface area, and volume concentrations] of ambient aerosol particles using multiwavelength Raman or high-spectral-resolution lidar. Using simple examples, we prove the non-uniqueness of an inverse solution to be the major source of the retrieval difficulties. Some theoretically possible ways of partially compensating for these difficulties are offered. For instance, an increase in the variety of input data via combination of lidar and certain passive remote sensing instruments will be helpful to reduce the error of estimation of the complex refractive index. We also demonstrate a significant interference between Aitken and accumulation aerosol modes in our inversion algorithm, and confirm that the solutions can be better constrained by limiting the particle radii. Applying a combination of an analytical approach and numerical simulations, we explain the statistical behavior of the microphysical size parameters. We reveal and clarify why the total surface area concentration is consistent even in the presence of non-unique solution sets and is on average the most stable parameter to be estimated, as long as at least one extinction optical coefficient is employed. We find that for selected particle size distributions, the total surface area and volume concentrations can be quickly retrieved with fair precision using only single extinction coefficients in a simple arithmetical relationship. PMID:27140552
NASA Astrophysics Data System (ADS)
Yang, Ying; Wei, Guangsheng
2016-09-01
The inverse spectral and scattering problems for the radial Schrödinger equation on the half-line {[0,∞)} are considered for a real-valued, integrable potential having a finite first moment. It is shown that the potential is uniquely determined in terms of the mixed spectral or scattering data which consist of the partial knowledge of the potential given on the finite interval {[0,ɛ]} for some {ɛ > 0} and either the amplitude or phase (being equivalent to scattering function) of the Jost function, without bound state data.
NASA Astrophysics Data System (ADS)
Filippi, Anthony Matthew
For complex systems, sufficient a priori knowledge is often lacking about the mathematical or empirical relationship between cause and effect or between inputs and outputs of a given system. Automated machine learning may offer a useful solution in such cases. Coastal marine optical environments represent such a case, as the optical remote sensing inverse problem remains largely unsolved. A self-organizing, cybernetic mathematical modeling approach known as the group method of data handling (GMDH), a type of statistical learning network (SLN), was used to generate explicit spectral inversion models for optically shallow coastal waters. Optically shallow water light fields represent a particularly difficult challenge in oceanographic remote sensing. Several algorithm-input data treatment combinations were utilized in multiple experiments to automatically generate inverse solutions for various inherent optical property (IOP), bottom optical property (BOP), constituent concentration, and bottom depth estimations. The objective was to identify the optimal remote-sensing reflectance Rrs(lambda) inversion algorithm. The GMDH also has the potential of inductive discovery of physical hydro-optical laws. Simulated data were used to develop generalized, quasi-universal relationships. The Hydrolight numerical forward model, based on radiative transfer theory, was used to compute simulated above-water remote-sensing reflectance Rrs(lambda) psuedodata, matching the spectral channels and resolution of the experimental Naval Research Laboratory Ocean PHILLS (Portable Hyperspectral Imager for Low-Light Spectroscopy) sensor. The input-output pairs were for GMDH and artificial neural network (ANN) model development, the latter of which was used as a baseline, or control, algorithm. Both types of models were applied to in situ and aircraft data. Also, in situ spectroradiometer-derived Rrs(lambda) were used as input to an optimization-based inversion procedure. Target variables
General solution of the inverse problem of the calculus of variations
Tonti, E.
1982-06-01
Using the operator notation, we show that for every nonlinera problem there exists a variational formulation and, much more an infinity of them. The corresponding functionals are easily obtained. It follows that the requirement of the existence of a variational formulation for a given problem as a heuristic criterion to select admissible field equations, or equations of motion, or interactions, is to be abandoned.
NASA Astrophysics Data System (ADS)
Wyatt, Philip
2009-03-01
The electromagnetic inverse scattering problem suggests that if a homogeneous and non-absorbing object be illuminated with a monochromatic light source and if the far field scattered light intensity is known at sufficient scattering angles, then, in principle, one could derive the dielectric structure of the scattering object. In general, this is an ill-posed problem and methods must be developed to regularize the search for unique solutions. An iterative procedure often begins with a model of the scattering object, solves the forward scattering problem using this model, and then compares these calculated results with the measured values. Key to any such solution is instrumentation capable of providing adequate data. To this end, the development of the first laser based absolute light scattering photometers is described together with their continuing evolution and some of the remarkable discoveries made with them. For particles much smaller than the wavelength of the incident light (e.g. macromolecules), the inverse scattering problems are easily solved. Among the many solutions derived with this instrumentation are the in situ structure of bacterial cells, new drug delivery mechanisms, the development of new vaccines and other biologicals, characterization of wines, the possibility of custom chemotherapy, development of new polymeric materials, identification of protein crystallization conditions, and a variety discoveries concerning protein interactions. A new form of the problem is described to address bioterrorist threats. Over the many years of development and refinement, one element stands out as essential for the successes that followed: the R and D teams were always directed and executed by physics trained theorists and experimentalists. 14 Ph. D. physicists each made his/her unique contribution to the development of these evolving instruments and the interpretation of their results.
Porr, Bernd; von Ferber, Christian; Wörgötter, Florentin
2003-04-01
In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed reflex reaction, which has the objective disadvantage that it can react only after a disturbance has occurred. ISO learning eliminates this disadvantage by replacing the reflex-loop reactions with earlier anticipatory actions. In this article, we analytically demonstrate that this process can be understood in terms of control theory, showing that the system learns the inverse controller of its own reflex. Thereby, this system is able to learn a simple form of feedforward motor control. PMID:12689390
Zero-order bows in radially inhomogeneous spheres: direct and inverse problems.
Adam, John A
2011-10-01
Zero-order ray paths are examined in radially inhomogeneous spheres with differentiable refractive index profiles. It is demonstrated that zero-order and sometimes twin zero-order bows can exist when the gradient of refractive index is sufficiently negative. Abel inversion is used to "recover" the refractive index profiles; it is therefore possible in principle to specify the nature and type of bows and determine the refractive index profile that induces them. This may be of interest in the field of rainbow refractometry and optical fiber studies. This ray-theoretic analysis has direct similarities with the phenomenon of "orbiting" and other phenomena in scattering theory and also in seismological, surface gravity wave, and gravitational "lensing" studies. For completeness these topics are briefly discussed in the appendixes; they may also be of pedagogic interest. PMID:22016245
Solving spatial inverse problems using the probability perturbation method: An S-GEMS implementation
NASA Astrophysics Data System (ADS)
Li, Ting; Caers, Jef
2008-09-01
The probability perturbation method (PPM) is introduced as a flexible and efficient sampling technique for generating inverse solutions under a given prior geological constraint (prior model). In this paper, we present a methodology for producing software code that runs PPM within a public domain geostatistical software called the Stanford Geostatistical Earth Modeling Software (S-GEMS). The challenge in creating such code lies in the great diversity of forward models as well as prior models that can be handled by the PPM. Therefore, our software solution must be highly flexible and extensible such that it can be tailored to the various applications at hand. Our implementation has two main objectives: (1) to create an integrated working environment which provides users easy access to functionalities of the PPM through a general user interface as well as visualize results; (2) allow the users to plug-in their application specific code into the PPM algorithm workflow. We provide a two-part solution. The first part, which is hard-coded in S-GEMS as a plug-in module, runs the Dekker-Brent optimization algorithm to control the parameter perturbation needed for the inversion. It generates the PPM user interface and allows visualization of the spatial domain of interest using S-GEMS graphics capability. The second part is coded in object-oriented Python scripts and is used to control the PPM execution in S-GEMS. Users can program their particular needs in scripts and load them into S-GEMS as part of the PPM workflow. The same mechanism can be used to extend the capabilities of PPM itself by implementing new PPM variants in Python and making them a part of the base class hierarchy. Case studies are used to demonstrate the flexibility of our code. This approach requires the user to adapt only a small amount of python code, without modifying, or re-compiling the core S-GEMS code.
NASA Astrophysics Data System (ADS)
Ruggeri, Paolo; Irving, James; Holliger, Klaus
2015-08-01
We critically examine the performance of sequential geostatistical resampling (SGR) as a model proposal mechanism for Bayesian Markov-chain-Monte-Carlo (MCMC) solutions to near-surface geophysical inverse problems. Focusing on a series of simple yet realistic synthetic crosshole georadar tomographic examples characterized by different numbers of data, levels of data error and degrees of model parameter spatial correlation, we investigate the efficiency of three different resampling strategies with regard to their ability to generate statistically independent realizations from the Bayesian posterior distribution. Quite importantly, our results show that, no matter what resampling strategy is employed, many of the examined test cases require an unreasonably high number of forward model runs to produce independent posterior samples, meaning that the SGR approach as currently implemented will not be computationally feasible for a wide range of problems. Although use of a novel gradual-deformation-based proposal method can help to alleviate these issues, it does not offer a full solution. Further, we find that the nature of the SGR is found to strongly influence MCMC performance; however no clear rule exists as to what set of inversion parameters and/or overall proposal acceptance rate will allow for the most efficient implementation. We conclude that although the SGR methodology is highly attractive as it allows for the consideration of complex geostatistical priors as well as conditioning to hard and soft data, further developments are necessary in the context of novel or hybrid MCMC approaches for it to be considered generally suitable for near-surface geophysical inversions.
NASA Astrophysics Data System (ADS)
Fernández Martínez, Juan L.; García Gonzalo, Esperanza; Fernández Álvarez, José P.; Kuzma, Heidi A.; Menéndez Pérez, César O.
2010-05-01
PSO is an optimization technique inspired by the social behavior of individuals in nature (swarms) that has been successfully used in many different engineering fields. In addition, the PSO algorithm can be physically interpreted as a stochastic damped mass-spring system. This analogy has served to introduce the PSO continuous model and to deduce a whole family of PSO algorithms using different finite-differences schemes. These algorithms are characterized in terms of convergence by their respective first and second order stability regions. The performance of these new algorithms is first checked using synthetic functions showing a degree of ill-posedness similar to that found in many geophysical inverse problems having their global minimum located on a very narrow flat valley or surrounded by multiple local minima. Finally we present the application of these PSO algorithms to the analysis and solution of a VES inverse problem associated with a seawater intrusion in a coastal aquifer in southern Spain. PSO family members are successfully compared to other well known global optimization algorithms (binary genetic algorithms and simulated annealing) in terms of their respective convergence curves and the sea water intrusion depth posterior histograms.
NASA Astrophysics Data System (ADS)
Kholodtsova, Maria N.; Loschenov, Victor B.; Daul, Christian; Blondel, Walter
2014-05-01
Determining the optical properties of biological tissues in vivo from spectral intensity measurements performed at their surface is still a challenge. Based on spectroscopic data acquired, the aim is to solve an inverse problem, where the optical parameter values of a forward model are to be estimated through optimization procedure of some cost function. In many cases it is an ill-posed problem because of small numbers of measures, errors on experimental data, nature of a forward model output data, which may be affected by statistical noise in the case of Monte Carlo (MC) simulation or approximated values for short inter-fibre distances (for Diffusion Equation Approximation (DEA)). In case of optical biopsy, spatially resolved diffuse reflectance spectroscopy is one simple technique that uses various excitation-toemission fibre distances to probe tissue in depths. The aim of the present contribution is to study the characteristics of some classically used cost function, optimization methods (Levenberg-Marquardt algorithm) and how it is reaching global minimum when using MC and/or DEA approaches. Several methods of smoothing filters and fitting were tested on the reflectance curves, I(r), gathered from MC simulations. It was obtained that smoothing the initial data with local regression weighted second degree polynomial and then fitting the data with double exponential decay function decreases the probability of the inverse algorithm to converge to local minima close to the initial point of first guess.
Krasnopolsky, Vladimir M; Schiller, Helmut
2003-01-01
A broad class of neural network (NN) applications dealing with the remote measurements of geophysical (physical, chemical, and biological) parameters of the oceans, atmosphere, and land surface is presented. In order to infer these parameters from remote sensing (RS) measurements, standard retrieval and variational techniques are applied. Both techniques require a data converter (transfer function or forward model) to convert satellite measurements into geophysical parameters or vice versa. In many cases, the transfer function and the forward model can be represented as a continuous nonlinear mapping. Because the NN technique is a generic technique for nonlinear mapping, it can be used beneficially for modeling transfer functions and forward models. These applications are introduced in a broader framework of solving forward and inverse problems in RS. In this broader context, we show that NN is an appropriate and efficient tool for solving forward and inverse problems in RS and for developing fast and accurate forward models and accurate and robust retrieval algorithms. Theoretical considerations are illustrated by several real-life examples-operational NN applications developed by the authors for SSM/I and medium resolution imaging spectrometer sensors. PMID:12672428
NASA Astrophysics Data System (ADS)
Velimsky, J.
2011-12-01
Inversion of observatory and low-orbit satellite geomagnetic data in terms of the three-dimensional distribution of electrical conductivity in the Earth's mantle can provide an independent constraint on the physical, chemical, and mineralogical composition of the Earth's mantle. This problem has been recently approached by different numerical methods. There are several key challenges from the numerical and algorithmic point of view, in particular the accuracy and speed of the forward solver, the effective evaluation of sensitivities of data to changes of model parameters, and the dependence of results on the a-priori knowledge of the spatio-temporal structure of the primary ionospheric and magnetospheric electric currents. Here I present recent advancements of the time-domain, spherical harmonic-finite element approach. The forward solver has been adapted to distributed-memory parallel architecture using band-matrix routines from the ScaLapack library. The evaluation of gradient of data misfit in the model space using adjoint approach has been also paralellized. Finally, the inverse problem has been reformulated in a way which allows for simultaneous reconstruction of conductivity model and external field model directly from the data.
Scott, I; Phelps, G; Dalton, S
2014-12-01
Healthcare in Australia faces significant challenges. Variations in care, suboptimal safety and reliability, fragmentation of care and unsustainable cost increases are compounded by substantial overuse and underuse of clinical interventions. These problems arise not from intentional actions of individual clinicians, but from deficiencies in the design, operations and governance of systems of care. Physicians play an important role in optimising systems of care and, in doing so, must rely on enhanced skills in a range of domains. These include: how to evaluate and improve quality and safety of clinical processes; analyse and interpret clinical and administrative data in ways that can be used to enhance care delivery; build and lead cohesive multidisciplinary teams capable of solving operational defects and inefficient workarounds; and implement new and effective innovations in clinical service delivery. While clinical skills are essential in individual patient care, skills that improve systems of care targeting whole patient populations will become increasingly desirable and recognised as core skills. PMID:25442761
Eigenfunctions and Eigenvalues for a Scalar Riemann-Hilbert Problem Associated to Inverse Scattering
NASA Astrophysics Data System (ADS)
Pelinovsky, Dmitry E.; Sulem, Catherine
A complete set of eigenfunctions is introduced within the Riemann-Hilbert formalism for spectral problems associated to some solvable nonlinear evolution equations. In particular, we consider the time-independent and time-dependent Schrödinger problems which are related to the KdV and KPI equations possessing solitons and lumps, respectively. Non-standard scalar products, orthogonality and completeness relations are derived for these problems. The complete set of eigenfunctions is used for perturbation theory and bifurcation analysis of eigenvalues supported by the potentials under perturbations. We classify two different types of bifurcations of new eigenvalues and analyze their characteristic features. One type corresponds to thresholdless generation of solitons in the KdV equation, while the other predicts a threshold for generation of lumps in the KPI equation.
The {open_quotes}INVERSE PROBLEM{close_quotes} to the evaluation of the magnetic fields
Caspi, S.; Helm, M.; Laslett, L.J.
1995-06-01
In the design of superconducting magnet elements, such as may be required to guide and focus ions in a particle accelerator, one frequently premises some particular current distribution and then proceeds to compute the consequent magnetic field through use of the laws of Blot and Savart or of Ampere. When working in this manner one of course may need to revise frequently the postulated current distribution before arriving at a resulting magnetic field of acceptable field quality. It therefore is of interest to consider an alternative ({open_quotes}inverse{close_quotes}) procedure in which one specifies a desired character for the field required in the region interior to the winding and undertakes then to evaluate the current distribution on the specified winding surface that would provide this desired field. By evaluating the specified potential in the region interior to the winding along the interface, the authors have determined that a relaxation solution to the potential in the region outside the winding can be converged and used to calculate wire location. They have demonstrated this method by applying a slightly modified version of the program POISSON to a periodic alternating sinusoidal quadrupole field.
NASA Astrophysics Data System (ADS)
Cordier, G.; Choi, J.; Raguin, L. G.
2008-11-01
Skin microcirculation plays an important role in diseases such as chronic venous insufficiency and diabetes. Magnetic resonance imaging (MRI) can provide quantitative information with a better penetration depth than other noninvasive methods, such as laser Doppler flowmetry or optical coherence tomography. Moreover, successful MRI skin studies have recently been reported. In this article, we investigate three potential inverse models to quantify skin microcirculation using diffusion-weighted MRI (DWI), also known as q-space MRI. The model parameters are estimated based on nonlinear least-squares (NLS). For each of the three models, an optimal DWI sampling scheme is proposed based on D-optimality in order to minimize the size of the confidence region of the NLS estimates and thus the effect of the experimental noise inherent to DWI. The resulting covariance matrices of the NLS estimates are predicted by asymptotic normality and compared to the ones computed by Monte-Carlo simulations. Our numerical results demonstrate the effectiveness of the proposed models and corresponding DWI sampling schemes as compared to conventional approaches.
A unified framework for approximation in inverse problems for distributed parameter systems
NASA Technical Reports Server (NTRS)
Banks, H. T.; Ito, K.
1988-01-01
A theoretical framework is presented that can be used to treat approximation techniques for very general classes of parameter estimation problems involving distributed systems that are either first or second order in time. Using the approach developed, one can obtain both convergence and stability (continuous dependence of parameter estimates with respect to the observations) under very weak regularity and compactness assumptions on the set of admissible parameters. This unified theory can be used for many problems found in the recent literature and in many cases offers significant improvements to existing results.
NASA Astrophysics Data System (ADS)
Sjöberg, Lars E.
2013-06-01
In this study, we show that the traditionally defined Bouguer gravity anomaly needs a correction to become `the no-topography gravity anomaly' and that the isostatic gravity anomaly is better defined by the latter anomaly plus a gravity anomaly compensation effect than by the Bouguer gravity anomaly plus a gravitational compensation effect. This is because only the new isostatic gravity anomaly completely removes and compensates for the topographic effect. F. A. Vening Meinesz' inverse problem in isostasy deals with solving for the Moho depth from the known external gravity field and mean Moho depth (known, e.g. from seismic reflection data) by a regional isostatic compensation using a flat Earth approximation. H. Moritz generalized the problem to that of a global compensation with a spherical mean Earth approximation. The problem can be formulated mathematically as that of solving a non-linear Fredholm integral equation. The solutions to these problems are based on the condition of isostatic balance of the isostatic gravity anomaly, and, theoretically, this assumption cannot be met by the old definition of the isostatic gravity anomaly. We show how the Moho geometry can be solved for the gravity anomaly, gravity disturbance and disturbing potential, etc., and, from a theoretical point of view, all these solutions are the same.
Semenov, Alexander; Zaikin, Oleg
2016-01-01
In this paper we propose an approach for constructing partitionings of hard variants of the Boolean satisfiability problem (SAT). Such partitionings can be used for solving corresponding SAT instances in parallel. For the same SAT instance one can construct different partitionings, each of them is a set of simplified versions of the original SAT instance. The effectiveness of an arbitrary partitioning is determined by the total time of solving of all SAT instances from it. We suggest the approach, based on the Monte Carlo method, for estimating time of processing of an arbitrary partitioning. With each partitioning we associate a point in the special finite search space. The estimation of effectiveness of the particular partitioning is the value of predictive function in the corresponding point of this space. The problem of search for an effective partitioning can be formulated as a problem of optimization of the predictive function. We use metaheuristic algorithms (simulated annealing and tabu search) to move from point to point in the search space. In our computational experiments we found partitionings for SAT instances encoding problems of inversion of some cryptographic functions. Several of these SAT instances with realistic predicted solving time were successfully solved on a computing cluster and in the volunteer computing project SAT@home. The solving time agrees well with estimations obtained by the proposed method. PMID:27190753
Turbomachine blading with splitter blades designed by solving the inverse flow field problem
NASA Astrophysics Data System (ADS)
Luu, T. S.; Viney, B.; Bencherif, L.
1992-04-01
The paper presents an inverse method for the turbomachine blading design in incompressible non viscous flow in order to avoid cavitation and gives a new approach of the boundary conditions to be settled in relation with bound vorticity distribution on the blades. Treating first the 2D cascade design, it shows how the blade must be generated with the given thickness distribution and must be loaded in order to obtain the desired outlet flow angle. The 3D design is analysed by two steps S2-S1 approach proposed by Wu[1]. For the meridian flow (S2 approach), the blade thickness is taken into account by the modification of metric tensor in the continuity equation. The governing one is provided by the hub to shroud equilibrium condition and the meridian stream function is choosen to define the flow field. This step leads to the determination of axisymmetrical stream sheets as well as the approximate camber surface of the blades. In the second step, blade to blade flow (S1 approach) is analyzed. The governing equation is deduced from the momentum equation which implies that the vorticity of the absolute velocity must be tangential to the stream sheet. The bound vorticity distribution must be the same one as in S2 approach and the residual flux crossing over the blade be conservative (transpiration model). These two relations constitute the boundary conditions for the S1 flow. The detection of this residual flux due to the normal component of the relative velocity on the blade surface leads to the rectification of the camber surface. The optimized design of the blading of a centifugal impeller with splitter blades is presented. Pour définir la géométrie des aubages, les méthodes conventionnelles prennent la distribution de vitesse sur les deux faces de l'aube comme données initiales. En appliquant cette approche, on perd le contrôle de l'épaisseur de l'aube. Pour y remédier, la présente méthode suggère une méthode inverse en représentant les aubes par une
Approximation methods for inverse problems involving the vibration of beams with tip bodies
NASA Technical Reports Server (NTRS)
Rosen, I. G.
1984-01-01
Two cubic spline based approximation schemes for the estimation of structural parameters associated with the transverse vibration of flexible beams with tip appendages are outlined. The identification problem is formulated as a least squares fit to data subject to the system dynamics which are given by a hybrid system of coupled ordinary and partial differential equations. The first approximation scheme is based upon an abstract semigroup formulation of the state equation while a weak/variational form is the basis for the second. Cubic spline based subspaces together with a Rayleigh-Ritz-Galerkin approach were used to construct sequences of easily solved finite dimensional approximating identification problems. Convergence results are briefly discussed and a numerical example demonstrating the feasibility of the schemes and exhibiting their relative performance for purposes of comparison is provided.
NASA Astrophysics Data System (ADS)
Hunziker, J.; Thorbecke, J.; Slob, E. C.
2014-12-01
Commonly, electromagnetic measurements for exploring and monitoring hydrocarbon reservoirs are inverted for the subsurface conductivity distribution by minimizing the difference between the actual data and a forward modeled dataset. The convergence of the inversion process to the correct solution strongly depends on the shape of the solution space. Since this is a non-linear problem, there exist a multitude of minima of which only the global one provides the correct conductivity values. To easily find the global minimum we desire it to have a broad cone of attraction, while it should also feature a very narrow bottom in order to obtain the subsurface conductivity with high resolution. In this study, we aim to determine which combination of input data corresponds to a favorable shape of the solution space. Since the solution space is N-dimensional, with N being the number of unknown subsurface parameters, plotting it is out of the question. In our approach, we use a genetic algorithm (Goldberg, 1989) to probe the solution space. Such algorithms have the advantage that every run of the same problem will end up at a different solution. Most of these solutions are expected to lie close to the global minimum. A situation where only few runs end up in the global minimum indicates that the solution space consists of a lot of local minima or that the cone of attraction of the global minimum is small. If a lot of runs end up with a similar data-misfit but with a large spread of the subsurface medium parameters in one or more direction, it can be concluded that the chosen data-input is not sensitive with respect to that direction. Compared to the study of Hunziker et al. 2014, we allow also to invert for subsurface boundaries and include more combinations of input datasets. The results so far suggest that it is essential to include the magnetic field in the inversion process in order to find the anisotropic conductivity values. ReferencesGoldberg, D. E., 1989. Genetic
NASA Astrophysics Data System (ADS)
Yao, Zhewei; Hu, Zixi; Li, Jinglai
2016-07-01
Many scientific and engineering problems require to perform Bayesian inferences in function spaces, where the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular, when the function to infer is subject to sharp jumps, the commonly used Gaussian measures become unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite-dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Gaussian (TG) prior to address such problems, where the TV term is used to detect sharp jumps of the function, and the Gaussian distribution is used as a reference measure so that it results in a well-defined posterior measure in the function space. We also present an efficient Markov Chain Monte Carlo (MCMC) algorithm to draw samples from the posterior distribution of the TG prior. With numerical examples we demonstrate the performance of the TG prior and the efficiency of the proposed MCMC algorithm.
NASA Astrophysics Data System (ADS)
Giudici, Mauro; Casabianca, Davide; Comunian, Alessandro
2015-04-01
The basic classical inverse problem of groundwater hydrology aims at determining aquifer transmissivity (T ) from measurements of hydraulic head (h), estimates or measures of source terms and with the least possible knowledge on hydraulic transmissivity. The theory of inverse problems shows that this is an example of ill-posed problem, for which non-uniqueness and instability (or at least ill-conditioning) might preclude the computation of a physically acceptable solution. One of the methods to reduce the problems with non-uniqueness, ill-conditioning and instability is a tomographic approach, i.e., the use of data corresponding to independent flow situations. The latter might correspond to different hydraulic stimulations of the aquifer, i.e., to different pumping schedules and flux rates. Three inverse methods have been analyzed and tested to profit from the use of multiple sets of data: the Differential System Method (DSM), the Comparison Model Method (CMM) and the Double Constraint Method (DCM). DSM and CMM need h all over the domain and thus the first step for their application is the interpolation of measurements of h at sparse points. Moreover, they also need the knowledge of the source terms (aquifer recharge, well pumping rates) all over the aquifer. DSM is intrinsically based on the use of multiple data sets, which permit to write a first-order partial differential equation for T , whereas CMM and DCM were originally proposed to invert a single data set and have been extended to work with multiple data sets in this work. CMM and DCM are based on Darcy's law, which is used to update an initial guess of the T field with formulas based on a comparison of different hydraulic gradients. In particular, the CMM algorithm corrects the T estimate with ratio of the observed hydraulic gradient and that obtained with a comparison model which shares the same boundary conditions and source terms as the model to be calibrated, but a tentative T field. On the other hand
Cięszczyk, Sławomir; Kisała, Piotr
2016-02-20
We propose and experimentally demonstrate a method for the detection of steel material defects utilizing a fiber Bragg grating sensor. The considered defects are periodic grooves along the length of the tested steel profile. Direct measurement of the spectral reflectance characteristics of the fiber is performed, and the related inverse problem of indirect defect shape determination is solved. It has been demonstrated that the defect periodicity estimation is 2.5 mm, with an error of less than 0.1. Furthermore, it has been shown that for periodic intervals of the order of 5 mm, the difference between the strain amplitude calculated using our method and the amplitude obtained via the finite element method was 1.4 mϵ. PMID:26906595
NASA Astrophysics Data System (ADS)
Usui, Y.; Uehara, M.
2009-12-01
The scanning magnetometory reveals fine-scale magnetic field images over geological samples, offering unique paleomagnetic information. Recent scanning magnetometory reaches high moment sensitivity and high spatial resolution (less than 1 mm), owing to the high field-sensitivity sensors (e.g., SQUID, Magneto-Impedance (MI), Giant-Magneto-Resistance) and small sample-to-sensor distance. Using an MI sensor driven by low-noise circuit, we successfully obtained magnetic field images of thin-sectioned geological samples carrying natural remanent magnetization. The main challenge in the scanning magnetometory is to invert the obtained field data into a magnetization pattern (Weiss et al., 2007). Since the magnetization pattern is a continuous function of position, the magnetic inverse problem is essentially underdetermined. Consequently, there should always be limitations in the resolving power of the estimated magnetization. Assessing the resolving power is essential to properly interpret the solution of inverse problems. Nevertheless, this has not been done for the scanning magnetometory. In this study, we used the model-resolution to assess the problem. We developed software to calculate and visualize the model-resolution for a single target point using the Backus-Gilbert method and iterative least-square calculation. Examples using results obtained by our MI scanning magnetometer will be presented. Any solution to a linear inverse problem can be expressed as a linear combination of data. The corresponding combination of data kernel constructs an averaging kernel. Thus, any solution is a weighted average of the true magnetization pattern of the sample. In other words, the resolving power of the solution, or the model-resolution, can be assessed by drawing the averaging kernel. Since the scanning magnetometory measures 2-dimensional samples, the averaging kernel for single target point becomes 2-dimensioanl image. Preliminary calculation was performed on a field image
NASA Astrophysics Data System (ADS)
di Volo, Matteo; Burioni, Raffaella; Casartelli, Mario; Livi, Roberto; Vezzani, Alessandro
2016-01-01
We study the dynamics of networks with inhibitory and excitatory leak-integrate-and-fire neurons with short-term synaptic plasticity in the presence of depressive and facilitating mechanisms. The dynamics is analyzed by a heterogeneous mean-field approximation, which allows us to keep track of the effects of structural disorder in the network. We describe the complex behavior of different classes of excitatory and inhibitory components, which give rise to a rich dynamical phase diagram as a function of the fraction of inhibitory neurons. Using the same mean-field approach, we study and solve a global inverse problem: reconstructing the degree probability distributions of the inhibitory and excitatory components and the fraction of inhibitory neurons from the knowledge of the average synaptic activity field. This approach unveils new perspectives on the numerical study of neural network dynamics and the possibility of using these models as a test bed for the analysis of experimental data.
Inverse eigenvalue problems in vibration absorption: Passive modification and active control
NASA Astrophysics Data System (ADS)
Mottershead, John E.; Ram, Yitshak M.
2006-01-01
The abiding problem of vibration absorption has occupied engineering scientists for over a century and there remain abundant examples of the need for vibration suppression in many industries. For example, in the automotive industry the resolution of noise, vibration and harshness (NVH) problems is of extreme importance to customer satisfaction. In rotorcraft it is vital to avoid resonance close to the blade passing speed and its harmonics. An objective of the greatest importance, and extremely difficult to achieve, is the isolation of the pilot's seat in a helicopter. It is presently impossible to achieve the objectives of vibration absorption in these industries at the design stage because of limitations inherent in finite element models. Therefore, it is necessary to develop techniques whereby the dynamic of the system (possibly a car or a helicopter) can be adjusted after it has been built. There are two main approaches: structural modification by passive elements and active control. The state of art of the mathematical theory of vibration absorption is presented and illustrated for the benefit of the reader with numerous simple examples.
Blakeslee, Barbara; McCourt, Mark E.
2015-01-01
Research in lightness perception centers on understanding the prior assumptions and processing strategies the visual system uses to parse the retinal intensity distribution (the proximal stimulus) into the surface reflectance and illumination components of the scene (the distal stimulus—ground truth). It is agreed that the visual system must compare different regions of the visual image to solve this inverse problem; however, the nature of the comparisons and the mechanisms underlying them are topics of intense debate. Perceptual illusions are of value because they reveal important information about these visual processing mechanisms. We propose a framework for lightness research that resolves confusions and paradoxes in the literature, and provides insight into the mechanisms the visual system employs to tackle the inverse problem. The main idea is that much of the debate and confusion in the literature stems from the fact that lightness, defined as apparent reflectance, is underspecified and refers to three different types of judgments that are not comparable. Under stimulus conditions containing a visible illumination component, such as a shadow boundary, observers can distinguish and match three independent dimensions of achromatic experience: apparent intensity (brightness), apparent local intensity ratio (brightness-contrast), and apparent reflectance (lightness). In the absence of a visible illumination boundary, however, achromatic vision reduces to two dimensions and, depending on stimulus conditions and observer instructions, judgments of lightness are identical to judgments of brightness or brightness-contrast. Furthermore, because lightness judgments are based on different information under different conditions, they can differ greatly in their degree of difficulty and in their accuracy. This may, in part, explain the large variability in lightness constancy across studies. PMID:25954181
NASA Astrophysics Data System (ADS)
Ahn, Chi Young; Jeon, Kiwan; Park, Won-Kwang
2015-06-01
This study analyzes the well-known MUltiple SIgnal Classification (MUSIC) algorithm to identify unknown support of thin penetrable electromagnetic inhomogeneity from scattered field data collected within the so-called multi-static response matrix in limited-view inverse scattering problems. The mathematical theories of MUSIC are partially discovered, e.g., in the full-view problem, for an unknown target of dielectric contrast or a perfectly conducting crack with the Dirichlet boundary condition (Transverse Magnetic-TM polarization) and so on. Hence, we perform further research to analyze the MUSIC-type imaging functional and to certify some well-known but theoretically unexplained phenomena. For this purpose, we establish a relationship between the MUSIC imaging functional and an infinite series of Bessel functions of integer order of the first kind. This relationship is based on the rigorous asymptotic expansion formula in the existence of a thin inhomogeneity with a smooth supporting curve. Various results of numerical simulation are presented in order to support the identified structure of MUSIC. Although a priori information of the target is needed, we suggest a least condition of range of incident and observation directions to apply MUSIC in the limited-view problem.
NASA Astrophysics Data System (ADS)
Koepke, C.; Irving, J.
2015-12-01
Bayesian solutions to inverse problems in near-surface geophysics and hydrology have gained increasing popularity as a means of estimating not only subsurface model parameters, but also their corresponding uncertainties that can be used in probabilistic forecasting and risk analysis. In particular, Markov-chain-Monte-Carlo (MCMC) methods have attracted much recent attention as a means of statistically sampling from the Bayesian posterior distribution. In this regard, two approaches are commonly used to improve the computational tractability of the Bayesian-MCMC approach: (i) Forward models involving a simplification of the underlying physics are employed, which offer a significant reduction in the time required to calculate data, but generally at the expense of model accuracy, and (ii) the model parameter space is represented using a limited set of spatially correlated basis functions as opposed to a more intuitive high-dimensional pixel-based parameterization. It has become well understood that model inaccuracies resulting from (i) can lead to posterior parameter distributions that are highly biased and overly confident. Further, when performing model reduction as described in (ii), it is not clear how the prior distribution for the basis weights should be defined because simple (e.g., Gaussian or uniform) priors that may be suitable for a pixel-based parameterization may result in a strong prior bias when used for the weights. To address the issue of model error resulting from known forward model approximations, we generate a set of error training realizations and analyze them with principal component analysis (PCA) in order to generate a sparse basis. The latter is used in the MCMC inversion to remove the main model-error component from the residuals. To improve issues related to prior bias when performing model reduction, we also use a training realization approach, but this time models are simulated from the prior distribution and analyzed using independent
Elsheikh, Ahmed H.; Wheeler, Mary F.; Hoteit, Ibrahim
2014-02-01
A Hybrid Nested Sampling (HNS) algorithm is proposed for efficient Bayesian model calibration and prior model selection. The proposed algorithm combines, Nested Sampling (NS) algorithm, Hybrid Monte Carlo (HMC) sampling and gradient estimation using Stochastic Ensemble Method (SEM). NS is an efficient sampling algorithm that can be used for Bayesian calibration and estimating the Bayesian evidence for prior model selection. Nested sampling has the advantage of computational feasibility. Within the nested sampling algorithm, a constrained sampling step is performed. For this step, we utilize HMC to reduce the correlation between successive sampled states. HMC relies on the gradient of the logarithm of the posterior distribution, which we estimate using a stochastic ensemble method based on an ensemble of directional derivatives. SEM only requires forward model runs and the simulator is then used as a black box and no adjoint code is needed. The developed HNS algorithm is successfully applied for Bayesian calibration and prior model selection of several nonlinear subsurface flow problems.
NASA Astrophysics Data System (ADS)
Gurarslan, Gurhan; Karahan, Halil
2015-09-01
In this study, an accurate model was developed for solving problems of groundwater-pollution-source identification. In the developed model, the numerical simulations of flow and pollutant transport in groundwater were carried out using MODFLOW and MT3DMS software. The optimization processes were carried out using a differential evolution algorithm. The performance of the developed model was tested on two hypothetical aquifer models using real and noisy observation data. In the first model, the release histories of the pollution sources were determined assuming that the numbers, locations and active stress periods of the sources are known. In the second model, the release histories of the pollution sources were determined assuming that there is no information on the sources. The results obtained by the developed model were found to be better than those reported in literature.
Reconstruction from blind experimental data for an inverse problem for a hyperbolic equation
NASA Astrophysics Data System (ADS)
Beilina, Larisa; Thành, Nguyen Trung; Klibanov, Michael V.; Fiddy, Michael A.
2014-02-01
We consider the problem of the reconstruction of dielectrics from blind backscattered experimental data. The reconstruction is done from time domain data, as opposed to a more conventional case of frequency domain data. Experimental data were collected using a microwave scattering facility which was built at the University of North Carolina at Charlotte. This system sends electromagnetic pulses into the medium and collects the time-resolved backscattered data on a part of a plane. The spatially distributed dielectric constant \\varepsilon _{r}( \\mathbf {x}) ,\\mathbf {x}\\in {R}^{3} is the unknown coefficient of a wave-like PDE. This coefficient is reconstructed from those data in blind cases. To do this, a globally convergent numerical method is used.
Borghero, Francesco; Demontis, Francesco
2016-09-01
In the framework of geometrical optics, we consider the following inverse problem: given a two-parameter family of curves (congruence) (i.e., f(x,y,z)=c_{1},g(x,y,z)=c_{2}), construct the refractive-index distribution function n=n(x,y,z) of a 3D continuous transparent inhomogeneous isotropic medium, allowing for the creation of the given congruence as a family of monochromatic light rays. We solve this problem by following two different procedures: 1. By applying Fermat's principle, we establish a system of two first-order linear nonhomogeneous PDEs in the unique unknown function n=n(x,y,z) relating the assigned congruence of rays with all possible refractive-index profiles compatible with this family. Moreover, we furnish analytical proof that the family of rays must be a normal congruence. 2. By applying the eikonal equation, we establish a second system of two first-order linear homogeneous PDEs whose solutions give the equation S(x,y,z)=const. of the geometric wavefronts and, consequently, all pertinent refractive-index distribution functions n=n(x,y,z). Finally, we make a comparison between the two procedures described above, discussing appropriate examples having exact solutions. PMID:27607492
NASA Astrophysics Data System (ADS)
Cressie, N.; Wang, R.; Smyth, M.; Miller, C. E.
2016-05-01
Remote sensing of the atmosphere is typically achieved through measurements that are high-resolution radiance spectra. In this article, our goal is to characterize the first-moment and second-moment properties of the errors obtained when solving the regularized inverse problem associated with space-based atmospheric CO2 retrievals, specifically for the dry air mole fraction of CO2 in a column of the atmosphere. The problem of estimating (or retrieving) state variables is usually ill posed, leading to a solution based on regularization that is often called Optimal Estimation (OE). The difference between the estimated state and the true state is defined to be the retrieval error; error analysis for OE uses a linear approximation to the forward model, resulting in a calculation where the first moment of the retrieval error (the bias) is identically zero. This is inherently unrealistic and not seen in real or simulated retrievals. Nonzero bias is expected since the forward model of radiative transfer is strongly nonlinear in the atmospheric state. In this article, we extend and improve OE's error analysis based on a first-order, multivariate Taylor series expansion, by inducing the second-order terms in the expansion. Specifically, we approximate the bias through the second derivative of the forward model, which results in a formula involving the Hessian array. We propose a stable estimate of it, from which we obtain a second-order expression for the bias and the mean square prediction error of the retrieval.
NASA Astrophysics Data System (ADS)
Cipolatti, Rolci; Yamamoto, Masahiro
2011-09-01
We consider a solution u(p, g, a, b) to an initial value-boundary value problem for a wave equation: \\fl \\partial _t^2 u(x,t) = \\Delta u(x,t) + p(x)u(x,t), \\qquad x \\in \\Omega,\\qquad \\thinspace 0 < t < T\\\\ \\fl u(x,0) = a(x), \\qquad \\partial _tu(x,0) = b(x), \\qquad x \\in \\Omega,\\\\ \\fl u(x,t) = g(x,t), \\qquad x \\in \\partial \\Omega,\\qquad 0 < t < T, and we discuss an inverse problem of determining a coefficient p(x) and a, b by observations of u(p, g, a, b)(x, t) in a neighbourhood ω of ∂Ω over a time interval (0, T) and ∂itu(p, g, a, b)(x, T0), x in Ω, i = 0, 1, with T0 < T. We prove that if T - T0 and T0 are larger than the diameter of Ω, then we can choose a finite number of Dirichlet boundary inputs g1, ..., gN, so that the mapping \\fl \\lbrace u(p,g_j,a_j,b_j)\\vert _{\\omega \\times (0,T)}, \\partial _t^iu(p,g_j,a_j,b_j)(\\cdot,T_0)\\rbrace _{i=0,1, 1\\le j\\le N}\
NASA Astrophysics Data System (ADS)
Ouwerling, G. J. L.
1991-02-01
The nondestructive and experimentally straightforward capacitance-voltage method for doping profile determination has always been impaired by certain weakness. The abrupt depletion approximation introduces error for steep profiles; the required differentiation of the measurement data causes a considerable noise sensitivity. More fundamentally, the method is restricted to one spatial dimension, perpendicular to the wafer surface. To overcome these limitations, in this paper the use of a numerical inverse method for the interpretation of the measurement data is presented. The method inspired by the use of similar methods for material profiling problems in biophysics and geophysics. It is specific for doping profiling problems and involves the iterative solution of a linear least squares system of equations. In this system, the known vector is formed by the measured capacitance values, and the unknown vector by the discretization of the doping profile on a grid in the measurement device. The matrix elements are found by the solution of Poisson's equation for each measurement bias case. To resolve possible ill-posedness, the system is solved by singular value decomposition of the least squares matrix. The validity of the method is verified and its error sensitivity studied by applying it to the reconstruction of both one- and two-dimensional doping profiles from synthetic measurement data. A test structure suitable for two-dimensional doping profiling, the Trimos device, is proposed and investigated by numerical experiments.
NASA Astrophysics Data System (ADS)
Xue, Haile; Shen, Xueshun; Chou, Jifan
2015-10-01
Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model (model error, denoted as ME). In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature (SST) were both based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.
NASA Astrophysics Data System (ADS)
Jiminez-Rodriguez, Luis O.; Rodriguez-Diaz, Eladio; Velez-Reyes, Miguel; DiMarzio, Charles A.
2003-05-01
Hyperspectral Remote Sensing has the potential to be used as an effective coral monitoring system from space. The problems to be addressed in hyperspectral imagery of coastal waters are related to the medium, clutter, and the object to be detected. In coastal waters the variability due to the interaction between the coast and the sea can bring significant disparity in the optical properties of the water column and the sea bottom. In terms of the medium, there is high scattering and absorption. Related to clutter we have the ocean floor, dissolved salt and gases, and dissolved organic matter. The object to be detected, in this case the coral reefs, has a weak signal, with temporal and spatial variation. In real scenarios the absorption and backscattering coefficients have spatial variation due to different sources of variability (river discharge, different depths of shallow waters, water currents) and temporal fluctuations. The retrieval of information about an object beneath some medium with high scattering and absorption properties requires the development of mathematical models and processing tools in the area of inversion, image reconstruction and detection. This paper presents the development of algorithms for retrieving information and its application to the recognition and classification of coral reefs under water with particles that provide high absorption and scattering. The data was gathered using a high resolution imaging spectrometer (hyperspectral) sensor. A mathematical model that simplifies the radiative transfer equation was used to quantify the interaction between the object of interest, the medium and the sensor. Tikhonov method of regularization was used in the inversion process to estimate the bottom albedo, ρ, of the ocean floor using a priori information. The a priori information is in the form of measured spectral signatures of objects of interest, such as sand, corals, and sea grass.
NASA Astrophysics Data System (ADS)
Hansen, Thomas Mejer; Cordua, Knud Skou; Looms, Majken Caroline; Mosegaard, Klaus
2013-03-01
We present an application of the SIPPI Matlab toolbox, to obtain a sample from the a posteriori probability density function for the classical tomographic inversion problem. We consider a number of different forward models, linear and non-linear, such as ray based forward models that rely on the high frequency approximation of the wave-equation and 'fat' ray based forward models relying on finite frequency theory. In order to sample the a posteriori probability density function we make use of both least squares based inversion, for linear Gaussian inverse problems, and the extended Metropolis sampler, for non-linear non-Gaussian inverse problems. To illustrate the applicability of the SIPPI toolbox to a tomographic field data set we use a cross-borehole traveltime data set from Arrenæs, Denmark. Both the computer code and the data are released in the public domain using open source and open data licenses. The code has been developed to facilitate inversion of 2D and 3D travel time tomographic data using a wide range of possible a priori models and choices of forward models.
NASA Astrophysics Data System (ADS)
Zakharova, Natalia; Piskovatsky, Nicolay; Gusev, Anatoly
2014-05-01
Development of Informational-Computational Systems (ICS) for data assimilation procedures is one of multidisciplinary problems. To study and solve these problems one needs to apply modern results from different disciplines and recent developments in: mathematical modeling; theory of adjoint equations and optimal control; inverse problems; numerical methods theory; numerical algebra and scientific computing. The above problems are studied in the Institute of Numerical Mathematics of the Russian Academy of Science (INM RAS) in ICS for personal computers. In this work the results on the Special data base development for ICS "INM RAS - Black Sea" are presented. In the presentation the input information for ICS is discussed, some special data processing procedures are described. In this work the results of forecast using ICS "INM RAS - Black Sea" with operational observation data assimilation are presented. This study was supported by the Russian Foundation for Basic Research (project No 13-01-00753) and by Presidium Program of Russian Academy of Sciences (project P-23 "Black sea as an imitational ocean model"). References 1. V.I. Agoshkov, M.V. Assovskii, S.A. Lebedev, Numerical simulation of Black Sea hydrothermodynamics taking into account tide-forming forces. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 5-31. 2. E.I. Parmuzin, V.I. Agoshkov, Numerical solution of the variational assimilation problem for sea surface temperature in the model of the Black Sea dynamics. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 69-94. 3. V.B. Zalesny, N.A. Diansky, V.V. Fomin, S.N. Moshonkin, S.G. Demyshev, Numerical model of the circulation of Black Sea and Sea of Azov. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 95-111. 4. Agoshkov V.I.,Assovsky M.B., Giniatulin S. V., Zakharova N.B., Kuimov G.V., Parmuzin E.I., Fomin V.V. Informational Computational system of variational assimilation of observation data "INM RAS - Black sea"// Ecological
Meschiari, Stefano; Laughlin, Gregory P.
2010-07-20
Transit timing variations (TTVs)-deviations from strict periodicity between successive passages of a transiting planet-can be used to probe the structure and dynamics of multiple-planet systems. In this paper, we examine prospects for numerically solving the so-called inverse problem, the determination of the orbital elements of a perturbing body from the TTVs it induces. We assume that the planetary systems under examination have a limited number of Doppler velocity measurements and show that a more extensive radial velocity (RV) characterization with precision comparable to the semi-amplitude of the perturber may remove degeneracies in the solution. We examine several configurations of interest, including (1) a prototypical non-resonant system, modeled after HD 40307 b and c, which contains multiple super-Earth-mass planets, (2) a hypothetical system containing a transiting giant planet with a terrestrial-mass companion trapped in low-order mean motion resonance, and (3) the HAT-P-13 system, in which forced precession by an outer perturbing body that is well characterized by Doppler RV measurements can give insight into the interior structure of a perturbing planet, and for which the determination of mutual inclination between the transiting planet and its perturber is a key issue.
NASA Astrophysics Data System (ADS)
Angelo, Joseph P.; Bigio, Irving J.; Gioux, Sylvain
2016-03-01
Imaging technologies working in the spatial frequency domain are becoming increasingly popular for generating wide-field optical property maps, enabling further analysis of tissue parameters such as absorption or scattering. While acquisition methods have witnessed a very rapid growth and are now performing in real-time, processing methods are yet slow preventing information to be acquired and displayed in real-time. In this work, we present solutions for rapid inverse problem solving for optical properties by use of advanced look-up tables. In particular, we present methods and results from a dense, linearized look-up table and an analytical representation that currently run 100 times faster than the standard method and within 10% in both absorption and scattering. With the resulting computation time in the tens of milliseconds range, the proposed techniques enable video-rate feedback of real-time techniques such as snapshot of optical properties (SSOP) imaging, making full video-rate guidance in the clinic possible.
Dixneuf, Sophie; Rachet, Florent; Chrysos, Michael
2015-02-28
Owing in part to the p orbitals of its filled L shell, neon has repeatedly come on stage for its peculiar properties. In the context of collision-induced Raman spectroscopy, in particular, we have shown, in a brief report published a few years ago [M. Chrysos et al., Phys. Rev. A 80, 054701 (2009)], that the room-temperature anisotropic Raman lineshape of Ne-Ne exhibits, in the far wing of the spectrum, a peculiar structure with an aspect other than a smooth wing (on a logarithmic plot) which contrasts with any of the existing studies, and whose explanation lies in the distinct way in which overlap and exchange interactions interfere with the classical electrostatic ones in making the polarizability anisotropy, α∥ - α⊥. Here, we delve deeper into that study by reporting data for that spectrum up to 450 cm(-1) and for even- and odd-order spectral moments up to M6, as well as quantum lineshapes, generated from SCF, CCSD, and CCSD(T) models for α∥ - α⊥, which are critically compared with the experiment. On account of the knowledge of the spectrum over the augmented frequency domain, we show how the inverse scattering problem can be tackled both effectively and economically, and we report an analytic function for the anisotropy whose quantum lineshape faithfully reproduces our observations. PMID:25725726
A full-Bayesian approach to the inverse problem for steady-state groundwater flow and heat transport
NASA Astrophysics Data System (ADS)
Jiang, Yefang; Woodbury, Allan D.
2006-12-01
The full (hierarchal) Bayesian approach proposed by Woodbury & Ulrych and Jiang et al. is extended to the inverse problem for 2-D steady-state groundwater flow and heat transport. A stochastic conceptual framework for the heat flow and groundwater flow is adopted. A perturbation of both the groundwater flow and the advection-conduction heat transport equations leads to a linear formulation between heads, temperature and logarithm transmissivity [denoted as ln (T)]. A Bayesian updating procedure similar to that of Woodbury & Ulrych can then be performed. This new algorithm is examined against a generic example through simulations. The prior mean, variance and integral scales of ln (T) (hyperparameters) are treated as random variables and their pdfs are determined from maximum entropy considerations. It is also assumed that the statistical properties of the noise in the hydraulic head and temperature measurements are also uncertain. Uncertainties in all pertinent hyperparameters are removed by marginalization. It is found that the use of temperature measurements is showed to further improve the ln (T) estimates for the test case in comparison to the updated ln (T) field conditioned on ln (T) and head data; the addition of temperature data without hydraulic head data to the update also aids refinement of the ln (T) field compared to simply interpolating ln (T) data alone these results suggest that temperature measurements are a promising data source for site characterization for heterogeneous aquifer, which can be accomplished through the full-Bayesian methodology.
NASA Astrophysics Data System (ADS)
Padois, Thomas; Gauthier, Philippe-Aubert; Berry, Alain
2014-12-01
Microphone arrays have become a standard technique to localize and quantify source in aeroacoustics. The simplest approach is the beamforming that provides noise source maps with large main lobe and strong side lobes at low frequency. Since a decade, the focus is set on deconvolution techniques such as DAMAS or Clean-SC. While the source map is clearly improved, these methods require a large computation time. In this paper, we propose a sound source localization technique based on an inverse problem with beamforming regularization matrix called Hybrid Method. With synthetic data, we show that the side lobes are removed and the main lobe is narrower. Moreover, if the sound noise source map provided by this method is used as input in the DAMAS process, the number of DAMAS iterations is highly reduced. The Hybrid Method is applied to experimental data obtained in a closed wind-tunnel. In both cases of acoustic or aeroacoustic data, the source is correctly detected. The proposed Hybrid Method is found simple to implement and the computation time is low if the number of scan points is reasonable.
Dixneuf, Sophie; Rachet, Florent; Chrysos, Michael
2015-02-28
Owing in part to the p orbitals of its filled L shell, neon has repeatedly come on stage for its peculiar properties. In the context of collision-induced Raman spectroscopy, in particular, we have shown, in a brief report published a few years ago [M. Chrysos et al., Phys. Rev. A 80, 054701 (2009)], that the room-temperature anisotropic Raman lineshape of Ne–Ne exhibits, in the far wing of the spectrum, a peculiar structure with an aspect other than a smooth wing (on a logarithmic plot) which contrasts with any of the existing studies, and whose explanation lies in the distinct way in which overlap and exchange interactions interfere with the classical electrostatic ones in making the polarizability anisotropy, α{sub ∥} − α{sub ⊥}. Here, we delve deeper into that study by reporting data for that spectrum up to 450 cm{sup −1} and for even- and odd-order spectral moments up to M{sub 6}, as well as quantum lineshapes, generated from SCF, CCSD, and CCSD(T) models for α{sub ∥} − α{sub ⊥}, which are critically compared with the experiment. On account of the knowledge of the spectrum over the augmented frequency domain, we show how the inverse scattering problem can be tackled both effectively and economically, and we report an analytic function for the anisotropy whose quantum lineshape faithfully reproduces our observations.
NASA Technical Reports Server (NTRS)
Kozdoba, L. A.; Krivoshei, F. A.
1985-01-01
The solution of the inverse problem of nonsteady heat conduction is discussed, based on finding the coefficient of the heat conduction and the coefficient of specific volumetric heat capacity. These findings are included in the equation used for the electrical model of this phenomenon.
Juhás, Pavol; Farrow, Christopher L; Yang, Xiaohao; Knox, Kevin R; Billinge, Simon J L
2015-11-01
A strategy is described for regularizing ill posed structure and nanostructure scattering inverse problems (i.e. structure solution) from complex material structures. This paper describes both the philosophy and strategy of the approach, and a software implementation, DiffPy Complex Modeling Infrastructure (DiffPy-CMI). PMID:26522405
NASA Astrophysics Data System (ADS)
Goretzki, Nora; Inbar, Nimrod; Siebert, Christian; Möller, Peter; Rosenthal, Eliyahu; Schneider, Michael; Magri, Fabien
2015-04-01
Salty and thermal springs exist along the lakeshore of the Sea of Galilee, which covers most of the Tiberias Basin (TB) in the northern Jordan- Dead Sea Transform, Israel/Jordan. As it is the only freshwater reservoir of the entire area, it is important to study the salinisation processes that pollute the lake. Simulations of thermohaline flow along a 35 km NW-SE profile show that meteoric and relic brines are flushed by the regional flow from the surrounding heights and thermally induced groundwater flow within the faults (Magri et al., 2015). Several model runs with trial and error were necessary to calibrate the hydraulic conductivity of both faults and major aquifers in order to fit temperature logs and spring salinity. It turned out that the hydraulic conductivity of the faults ranges between 30 and 140 m/yr whereas the hydraulic conductivity of the Upper Cenomanian aquifer is as high as 200 m/yr. However, large-scale transport processes are also dependent on other physical parameters such as thermal conductivity, porosity and fluid thermal expansion coefficient, which are hardly known. Here, inverse problems (IP) are solved along the NW-SE profile to better constrain the physical parameters (a) hydraulic conductivity, (b) thermal conductivity and (c) thermal expansion coefficient. The PEST code (Doherty, 2010) is applied via the graphical interface FePEST in FEFLOW (Diersch, 2014). The results show that both thermal and hydraulic conductivity are consistent with the values determined with the trial and error calibrations. Besides being an automatic approach that speeds up the calibration process, the IP allows to cover a wide range of parameter values, providing additional solutions not found with the trial and error method. Our study shows that geothermal systems like TB are more comprehensively understood when inverse models are applied to constrain coupled fluid flow processes over large spatial scales. References Diersch, H.-J.G., 2014. FEFLOW Finite
NASA Astrophysics Data System (ADS)
Li, Zhenhai; Nie, Chenwei; Yang, Guijun; Xu, Xingang; Jin, Xiuliang; Gu, Xiaohe
2014-10-01
Leaf area index (LAI) and LCC, as the two most important crop growth variables, are major considerations in management decisions, agricultural planning and policy making. Estimation of canopy biophysical variables from remote sensing data was investigated using a radiative transfer model. However, the ill-posed problem is unavoidable for the unique solution of the inverse problem and the uncertainty of measurements and model assumptions. This study focused on the use of agronomy mechanism knowledge to restrict and remove the ill-posed inversion results. For this purpose, the inversion results obtained using the PROSAIL model alone (NAMK) and linked with agronomic mechanism knowledge (AMK) were compared. The results showed that AMK did not significantly improve the accuracy of LAI inversion. LAI was estimated with high accuracy, and there was no significant improvement after considering AMK. The validation results of the determination coefficient (R2) and the corresponding root mean square error (RMSE) between measured LAI and estimated LAI were 0.635 and 1.022 for NAMK, and 0.637 and 0.999 for AMK, respectively. LCC estimation was significantly improved with agronomy mechanism knowledge; the R2 and RMSE values were 0.377 and 14.495 μg cm-2 for NAMK, and 0.503 and 10.661 μg cm-2 for AMK, respectively. Results of the comparison demonstrated the need for agronomy mechanism knowledge in radiative transfer model inversion.
Sanz, E.; Voss, C.I.
2006-01-01
Inverse modeling studies employing data collected from the classic Henry seawater intrusion problem give insight into several important aspects of inverse modeling of seawater intrusion problems and effective measurement strategies for estimation of parameters for seawater intrusion. Despite the simplicity of the Henry problem, it embodies the behavior of a typical seawater intrusion situation in a single aquifer. Data collected from the numerical problem solution are employed without added noise in order to focus on the aspects of inverse modeling strategies dictated by the physics of variable-density flow and solute transport during seawater intrusion. Covariances of model parameters that can be estimated are strongly dependent on the physics. The insights gained from this type of analysis may be directly applied to field problems in the presence of data errors, using standard inverse modeling approaches to deal with uncertainty in data. Covariance analysis of the Henry problem indicates that in order to generally reduce variance of parameter estimates, the ideal places to measure pressure are as far away from the coast as possible, at any depth, and the ideal places to measure concentration are near the bottom of the aquifer between the center of the transition zone and its inland fringe. These observations are located in and near high-sensitivity regions of system parameters, which may be identified in a sensitivity analysis with respect to several parameters. However, both the form of error distribution in the observations and the observation weights impact the spatial sensitivity distributions, and different choices for error distributions or weights can result in significantly different regions of high sensitivity. Thus, in order to design effective sampling networks, the error form and weights must be carefully considered. For the Henry problem, permeability and freshwater inflow can be estimated with low estimation variance from only pressure or only
NASA Astrophysics Data System (ADS)
Hart, Vern Philip, II
A methodology is presented for creating tomographic reconstructions from various projection data, and the relevance of the results to applications in atmospheric science and biomedical imaging is analyzed. The fundamental differences between transform and iterative methods are described and the properties of the imaging configurations are addressed. The presented results are particularly suited for highly ill-conditioned inverse problems in which the imaging data are restricted as a result of poor angular coverage, limited detector arrays, or insufficient access to an imaging region. The class of reconstruction algorithms commonly used in sparse tomography, the algebraic reconstruction techniques, is presented, analyzed, and compared. These algorithms are iterative in nature and their accuracy depends significantly on the initialization of the algorithm, the so-called initial guess. A considerable amount of research was conducted into novel initialization techniques as a means of improving the accuracy. The main body of this paper is comprised of three smaller papers, which describe the application of the presented methods to atmospheric and medical imaging modalities. The first paper details the measurement of mesospheric airglow emissions at two camera sites operated by Utah State University. Reconstructions of vertical airglow emission profiles are presented, including three-dimensional models of the layer formed using a novel fanning technique. The second paper describes the application of the method to the imaging of polar mesospheric clouds (PMCs) by NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. The contrasting elements of straight-line and diffusive tomography are also discussed in the context of ill-conditioned imaging problems. A number of developing modalities in medical tomography use near infrared light, which interacts strongly with biological tissue and results in significant optical scattering. In order to perform tomography on the
NASA Astrophysics Data System (ADS)
Klibanov, Michael V.; Timonov, Alexandre
2001-12-01
Some coefficient inverse problems of electromagnetic frequency sounding of inhomogeneous media are considered. Such problems occur in many areas of applied physics, such as the geophysical exploration of gas, oil and mineral deposits, reservoir monitoring, marine acoustics and electromagnetics, optical sensing, and radio physics. Reformulating these problems in terms of nonlinear least squares, also known in the applied literature as matched field processing, often leads to a multiextremal and multidimensional objective function. This makes it extremely difficult to find its global extremum which corresponds to the solution of the original problem. It is shown in this paper that an inverse problem of frequency sounding can first be identically transformed to a certain boundary value problem which does not explicitly contain an unknown coefficient. The nonlinear least squares are then applied to the transformed problem. Using the weight functions associated with the Carleman estimates for the Laplace operator, an objective function is constructed in such a way that it is strictly convex on a certain compact set. The feasibility of the proposed approach is demonstrated in computational experiments with a model problem of magnetotelluric frequency sounding of layered media.
Paracentric inversions do not normally generate monocentric recombinant chromosomes
Sutherland, G.R.; Callen, D.F.; Gardner, R.J.M.
1995-11-20
Dr. Pettenati et al. recently reported a review of paracentric inversions in humans in which they concluded that carriers of these have a 3.8% risk of viable offspring with recombinant chromosomes. We are of the view that there are serious problems with this estimate which should be much closer to zero. The only recombinant chromosomes which can be generated by a paracentric inversion undergoing a normal meiotic division are dicentrics and acentric fragments. Only two such cases were found by Pettenati et al. Several of the alleged monocentric recombinants were originally reported as arising from parental insertions (3-break rearrangements) and it is not legitimate to include them in any analysis of paracentric inversions. Any monocentric recombinant chromosome can only arise from a paracentric inversion by some abnormal process which must involve chromatid breakage and reunion. 4 refs.
NASA Astrophysics Data System (ADS)
Dorn, Oliver; Lionheart, Bill
2010-11-01
This proceeding combines selected contributions from participants of the Workshop on Electromagnetic Inverse Problems which was hosted by the University of Manchester in June 2009. The workshop was organized by the two guest editors of this conference proceeding and ran in parallel to the 10th International Conference on Electrical Impedance Tomography, which was guided by Bill Lionheart, Richard Bayford, and Eung Je Woo. Both events shared plenary talks and several selected sessions. One reason for combining these two events was the goal of bringing together scientists from various related disciplines who normally might not attend the same conferences, and to enhance discussions between these different groups. So, for example, one day of the workshop was dedicated to the broader area of geophysical inverse problems (including inverse problems in petroleum engineering), where participants from the EIT community and from the medical imaging community were also encouraged to participate, with great success. Other sessions concentrated on microwave medical imaging, on inverse scattering, or on eddy current imaging, with active feedback also from geophysically oriented scientists. Furthermore, several talks addressed such diverse topics as optical tomography, photoacoustic tomography, time reversal, or electrosensing fish. As a result of the workshop, speakers were invited to contribute extended papers to this conference proceeding. All submissions were thoroughly reviewed and, after a thoughtful revision by the authors, combined in this proceeding. The resulting set of six papers presenting the work of in total 22 authors from 5 different countries provides a very interesting overview of several of the themes which were represented at the workshop. These can be divided into two important categories, namely (i) modelling and (ii) data inversion. The first three papers of this selection, as outlined below, focus more on modelling aspects, being an essential component of
NASA Technical Reports Server (NTRS)
Voorhies, Coerte V.
1993-01-01
The problem of estimating a steady fluid velocity field near the top of Earth's core which induces the secular variation (SV) indicated by models of the observed geomagnetic field is examined in the source-free mantle/frozen-flux core (SFI/VFFC) approximation. This inverse problem is non-linear because solutions of the forward problem are deterministically chaotic. The SFM/FFC approximation is inexact, and neither the models nor the observations they represent are either complete or perfect. A method is developed for solving the non-linear inverse motional induction problem posed by the hypothesis of (piecewise, statistically) steady core surface flow and the supposition of a complete initial geomagnetic condition. The method features iterative solution of the weighted, linearized least-squares problem and admits optional biases favoring surficially geostrophic flow and/or spatially simple flow. Two types of weights are advanced radial field weights for fitting the evolution of the broad-scale portion of the radial field component near Earth's surface implied by the models, and generalized weights for fitting the evolution of the broad-scale portion of the scalar potential specified by the models.
Fuentes, D; Elliott, A; Weinberg, J S; Shetty, A; Hazle, J D; Stafford, R J
2013-01-01
Quantification of local variations in the optical properties of tumor tissue introduced by the presence of gold-silica nanoparticles (NP) presents significant opportunities in monitoring and control of NP-mediated laser induced thermal therapy (LITT) procedures. Finite element methods of inverse parameter recovery constrained by a Pennes bioheat transfer model were applied to estimate the optical parameters. Magnetic resonance temperature imaging (MRTI) acquired during a NP-mediated LITT of a canine transmissible venereal tumor in brain was used in the presented statistical inverse problem formulation. The maximum likelihood (ML) value of the optical parameters illustrated a marked change in the periphery of the tumor corresponding with the expected location of NP and area of selective heating observed on MRTI. Parameter recovery information became increasingly difficult to infer in distal regions of tissue where photon fluence had been significantly attenuated. Finite element temperature predictions using the ML parameter values obtained from the solution of the inverse problem are able to reproduce the NP selective heating within 5 °C of measured MRTI estimations along selected temperature profiles. Results indicate the ML solution found is able to sufficiently reproduce the selectivity of the NP mediated laser induced heating and therefore the ML solution is likely to return useful optical parameters within the region of significant laser fluence. PMID:22918665
NASA Astrophysics Data System (ADS)
Kozlovskaya, E.; Hjelt, S.-E.; Yliniemi, J.; Korhonen, J.; Elo, S.; Svekalapko Seismic Tomography Working Group
The SVEKALAPKO temporary seismic array was originally designed for studying the lithospheric structure beneath the southern Finland by means of teleseismic events. In addition, the high quality recordings of local seismic events (earthquakes and quarry blasts) registered by the SVEKALAPKO array contain a lot of information about the structure of the upper lithosphere beneath the southern Finland. However, the traditional local event tomography of SVEKALAPKO data cannot result in the ve- locity model valid for proper geological interpretation. The main reasons for this is the lack of the space resolution, which results in strong non-uniqueness of the to- mographic inversion. This problem can be treated by introducing various kinds of a-priori information into the inversion algorithm. The a-priori information available for the SVEKALAPKO study area includes not only the information from previous control source seismic experiments, but also potential fields data and petrophysical data. Usage of such a non-homogeneous a-priori information required a problem- dependent algorithm of seismic data inversion, which was developed on the base of non-probabilistic uncertainty measures. An example of this algorithm application to SVEKALAPKO local seismic event data is presented.
NASA Astrophysics Data System (ADS)
Dorn, Oliver; Lionheart, Bill
2010-11-01
This proceeding combines selected contributions from participants of the Workshop on Electromagnetic Inverse Problems which was hosted by the University of Manchester in June 2009. The workshop was organized by the two guest editors of this conference proceeding and ran in parallel to the 10th International Conference on Electrical Impedance Tomography, which was guided by Bill Lionheart, Richard Bayford, and Eung Je Woo. Both events shared plenary talks and several selected sessions. One reason for combining these two events was the goal of bringing together scientists from various related disciplines who normally might not attend the same conferences, and to enhance discussions between these different groups. So, for example, one day of the workshop was dedicated to the broader area of geophysical inverse problems (including inverse problems in petroleum engineering), where participants from the EIT community and from the medical imaging community were also encouraged to participate, with great success. Other sessions concentrated on microwave medical imaging, on inverse scattering, or on eddy current imaging, with active feedback also from geophysically oriented scientists. Furthermore, several talks addressed such diverse topics as optical tomography, photoacoustic tomography, time reversal, or electrosensing fish. As a result of the workshop, speakers were invited to contribute extended papers to this conference proceeding. All submissions were thoroughly reviewed and, after a thoughtful revision by the authors, combined in this proceeding. The resulting set of six papers presenting the work of in total 22 authors from 5 different countries provides a very interesting overview of several of the themes which were represented at the workshop. These can be divided into two important categories, namely (i) modelling and (ii) data inversion. The first three papers of this selection, as outlined below, focus more on modelling aspects, being an essential component of
Hierarchical Bayesian inverse reinforcement learning.
Choi, Jaedeug; Kim, Kee-Eung
2015-04-01
Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the expert's behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal. Another difficulty comes from the noisy behavior data due to sub-optimal experts. We propose a hierarchical Bayesian framework, which subsumes most of the previous IRL algorithms as well as models the sub-optimality of the expert's behavior. Using a number of experiments on a synthetic problem, we demonstrate the effectiveness of our approach including the robustness of our hierarchical Bayesian framework to the sub-optimal expert behavior data. Using a real dataset from taxi GPS traces, we additionally show that our approach predicts the driving behavior with a high accuracy. PMID:25291805
Sarode, Ketan Dinkar; Kumar, V Ravi; Kulkarni, B D
2016-05-01
An efficient inverse problem approach for parameter estimation, state and structure identification from dynamic data by embedding training functions in a genetic algorithm methodology (ETFGA) is proposed for nonlinear dynamical biosystems using S-system canonical models. Use of multiple shooting and decomposition approach as training functions has been shown for handling of noisy datasets and computational efficiency in studying the inverse problem. The advantages of the methodology are brought out systematically by studying it for three biochemical model systems of interest. By studying a small-scale gene regulatory system described by a S-system model, the first example demonstrates the use of ETFGA for the multifold aims of the inverse problem. The estimation of a large number of parameters with simultaneous state and network identification is shown by training a generalized S-system canonical model with noisy datasets. The results of this study bring out the superior performance of ETFGA on comparison with other metaheuristic approaches. The second example studies the regulation of cAMP oscillations in Dictyostelium cells now assuming limited availability of noisy data. Here, flexibility of the approach to incorporate partial system information in the identification process is shown and its effect on accuracy and predictive ability of the estimated model are studied. The third example studies the phenomenological toy model of the regulation of circadian oscillations in Drosophila that follows rate laws different from S-system power-law. For the limited noisy data, using a priori information about properties of the system, we could estimate an alternate S-system model that showed robust oscillatory behavior with predictive abilities. PMID:26968929
Saito, Shigeyoshi; Tanaka, Keiko; Hashido, Takashi
2016-02-01
The purpose of this study was to compare the mean hepatic stiffness values obtained by the application of two different direct inverse problem reconstruction methods to magnetic resonance elastography (MRE). Thirteen healthy men (23.2±2.1 years) and 16 patients with liver diseases (78.9±4.3 years; 12 men and 4 women) were examined for this study using a 3.0 T-MRI. The healthy volunteers underwent three consecutive scans, two 70-Hz waveform and a 50-Hz waveform scans. On the other hand, the patients with liver disease underwent scanning using the 70-Hz waveform only. The MRE data for each subject was processed twice for calculation of the mean hepatic stiffness (Pa), once using the multiscale direct inversion (MSDI) and once using the multimodel direct inversion (MMDI). There were no significant differences in the mean stiffness values among the scans obtained with two 70-Hz and different waveforms. However, the mean stiffness values obtained with the MSDI technique (with mask: 2895.3±255.8 Pa, without mask: 2940.6±265.4 Pa) were larger than those obtained with the MMDI technique (with mask: 2614.0±242.1 Pa, without mask: 2699.2±273.5 Pa). The reproducibility of measurements obtained using the two techniques was high for both the healthy volunteers [intraclass correlation coefficients (ICCs): 0.840-0.953] and the patients (ICC: 0.830-0.995). These results suggest that knowledge of the characteristics of different direct inversion algorithms is important for longitudinal liver stiffness assessments such as the comparison of different scanners and evaluation of the response to fibrosis therapy. PMID:26902377
NASA Astrophysics Data System (ADS)
Szyszkiewicz-Warzecha, Krzysztof; Jasielec, Jerzy J.; Fausek, Janusz; Filipek, Robert
2016-06-01
Transport properties of ions have significant impact on the possibility of rebars corrosion thus the knowledge of a diffusion coefficient is important for reinforced concrete durability. Numerous tests for the determination of diffusion coefficients have been proposed but analysis of some of these tests show that they are too simplistic or even not valid. Hence, more rigorous models to calculate the coefficients should be employed. Here we propose the Nernst-Planck and Poisson equations, which take into account the concentration and electric potential field. Based on this model a special inverse method is presented for determination of a chloride diffusion coefficient. It requires the measurement of concentration profiles or flux on the boundary and solution of the NPP model to define the goal function. Finding the global minimum is equivalent to the determination of diffusion coefficients. Typical examples of the application of the presented method are given.
NASA Astrophysics Data System (ADS)
Kochukhov, O.; Tsymbal, V.; Ryabchikova, T.; Makaganyk, V.; Bagnulo, S.
2006-12-01
Context: .High spectral resolution studies of cool Ap stars reveal conspicuous anomalies of the shape and strength of many absorption lines. This is a signature of large atmospheric chemical gradients (chemical stratification) produced by the selective radiative levitation and gravitational settling of chemical species. Aims: .Previous observational studies of the chemical stratification in Ap stars were limited to fitting simple parametrized chemical profiles. Here we present a new approach to mapping the vertical chemical structures in stellar atmospheres. Methods: .We have developed a regularized chemical inversion procedure that uses all information available in high-resolution stellar spectra. The new technique for the first time allowed us to recover chemical profiles without making a priori assumptions about the shape of chemical distributions. We have derived average abundances and applied the vertical inversion procedure to the high-resolution VLT UVES spectra of the weakly magnetic, cool Ap star HD 133792. Results: .Our spectroscopic analysis yielded improved estimates of the atmospheric parameters of HD 133792. We show that this star has negligible v_e sin i and the mean magnetic field modulus < B > = 1.1 ± 0.1 kG. We have derived average abundances for 43 ions and obtained vertical distributions of Ca, Si, Mg, Fe, Cr, and Sr. All these elements except Mg show high overabundance in the deep layers and solar or sub-solar composition in the upper atmosphere of HD 133792. In contrast, the Mg abundance increases with height. Conclusions: .We find that transition from the metal-enhanced to metal-depleted zones typically occurs in a rather narrow range of depths in the atmosphere of HD 133792. Based on the derived photospheric abundances, we conclude that HD 133792 belongs to the rare group of evolved cool Ap stars, which possesses very large Fe-peak enhancement, but lacks a prominent overabundance of the rare-earth elements.
Kozunov, Vladimir V; Ossadtchi, Alexei
2015-01-01
Although MEG/EEG signals are highly variable between subjects, they allow characterizing systematic changes of cortical activity in both space and time. Traditionally a two-step procedure is used. The first step is a transition from sensor to source space by the means of solving an ill-posed inverse problem for each subject individually. The second is mapping of cortical regions consistently active across subjects. In practice the first step often leads to a set of active cortical regions whose location and timecourses display a great amount of interindividual variability hindering the subsequent group analysis. We propose Group Analysis Leads to Accuracy (GALA)-a solution that combines the two steps into one. GALA takes advantage of individual variations of cortical geometry and sensor locations. It exploits the ensuing variability in electromagnetic forward model as a source of additional information. We assume that for different subjects functionally identical cortical regions are located in close proximity and partially overlap and their timecourses are correlated. This relaxed similarity constraint on the inverse solution can be expressed within a probabilistic framework, allowing for an iterative algorithm solving the inverse problem jointly for all subjects. A systematic simulation study showed that GALA, as compared with the standard min-norm approach, improves accuracy of true activity recovery, when accuracy is assessed both in terms of spatial proximity of the estimated and true activations and correct specification of spatial extent of the activated regions. This improvement obtained without using any noise normalization techniques for both solutions, preserved for a wide range of between-subject variations in both spatial and temporal features of regional activation. The corresponding activation timecourses exhibit significantly higher similarity across subjects. Similar results were obtained for a real MEG dataset of face-specific evoked responses
Kozunov, Vladimir V.; Ossadtchi, Alexei
2015-01-01
Although MEG/EEG signals are highly variable between subjects, they allow characterizing systematic changes of cortical activity in both space and time. Traditionally a two-step procedure is used. The first step is a transition from sensor to source space by the means of solving an ill-posed inverse problem for each subject individually. The second is mapping of cortical regions consistently active across subjects. In practice the first step often leads to a set of active cortical regions whose location and timecourses display a great amount of interindividual variability hindering the subsequent group analysis. We propose Group Analysis Leads to Accuracy (GALA)—a solution that combines the two steps into one. GALA takes advantage of individual variations of cortical geometry and sensor locations. It exploits the ensuing variability in electromagnetic forward model as a source of additional information. We assume that for different subjects functionally identical cortical regions are located in close proximity and partially overlap and their timecourses are correlated. This relaxed similarity constraint on the inverse solution can be expressed within a probabilistic framework, allowing for an iterative algorithm solving the inverse problem jointly for all subjects. A systematic simulation study showed that GALA, as compared with the standard min-norm approach, improves accuracy of true activity recovery, when accuracy is assessed both in terms of spatial proximity of the estimated and true activations and correct specification of spatial extent of the activated regions. This improvement obtained without using any noise normalization techniques for both solutions, preserved for a wide range of between-subject variations in both spatial and temporal features of regional activation. The corresponding activation timecourses exhibit significantly higher similarity across subjects. Similar results were obtained for a real MEG dataset of face-specific evoked responses