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1

Successful combination of the stochastic linearization and Monte Carlo methods  

NASA Technical Reports Server (NTRS)

A combination of a stochastic linearization and Monte Carlo techniques is presented for the first time in literature. A system with separable nonlinear damping and nonlinear restoring force is considered. The proposed combination of the energy-wise linearization with the Monte Carlo method yields an error under 5 percent, which corresponds to the error reduction associated with the conventional stochastic linearization by a factor of 4.6.

Elishakoff, I.; Colombi, P.

1993-01-01

2

Monte Carlo Monte Carlo  

E-print Network

Monte Carlo Monte Carlo at Work by Gary D. Doolen and John Hendricks E very second nearly 10,000,000,000 "random" numbers are being generated on computers around the world for Monte Carlo solutions to problems hundreds of full-time careers invested in the fine art of generating Monte Carlo solutions--a livelihood

3

Stochastic approximation Monte Carlo and Wang-Landau Monte Carlo applied to a continuum polymer model  

NASA Astrophysics Data System (ADS)

We discuss Stochastic Approximation Monte Carlo (SAMC) simulations, and Wang-Landau Monte Carlo (WLMC) simulations as one form of SAMC simulations, in an application to determine the density of states of a class of continuum polymer models. WLMC has been established in the literature as a powerful tool to determine the density of states of polymer models, but it has also been established that not all versions of WLMC really converge to the desired density of states. Convergence of SAMC simulations has been established in the mathematical literature and discussing WLMC as a special case of SAMC brings a clearer perspective to the properties of WLMC. On the other hand, practical convergence of SAMC simulations with a fixed simulation effort needs to be established for given physical problems and, for practical applications, the relative efficiency and accuracy of the two approaches need to be compared.

Werlich, B.; Shakirov, T.; Taylor, M. P.; Paul, W.

2015-01-01

4

Wang-Landau and Stochastic Approximation Monte Carlo for Semi-flexible Polymer Chains  

NASA Astrophysics Data System (ADS)

We present a comparison of the performance, relative strengths and relative weaknesses of standard Wang-Landau Monte Carlo simulations and Stochastic Approximation Monte Carlo simulations applied to semi-flexible single polymer chains.

Werlich, B.; Taylor, M. P.; Paul, W.

5

Theory and algorithms for mixed Monte Carlo-stochastic dynamics simulations  

Microsoft Academic Search

The recently introduced mixed MC-SD method is a fundamentally new procedure which essentially eliminates the distinction between Monte Carlo and dynamics. Unlike other methods which utilize forces, Brownian motion or dynamical steps to generate new trial configurations in a Monte Carlo search, mixed MC-SD does stochastic dynamics on the cartesian space of a molecule and Monte Carlo on the torsion

Frank Guarnieri; Mount Sinai

1995-01-01

6

Optimization of Monte Carlo transport simulations in stochastic media  

SciTech Connect

This paper presents an accurate and efficient approach to optimize radiation transport simulations in a stochastic medium of high heterogeneity, like the Very High Temperature Gas-cooled Reactor (VHTR) configurations packed with TRISO fuel particles. Based on a fast nearest neighbor search algorithm, a modified fast Random Sequential Addition (RSA) method is first developed to speed up the generation of the stochastic media systems packed with both mono-sized and poly-sized spheres. A fast neutron tracking method is then developed to optimize the next sphere boundary search in the radiation transport procedure. In order to investigate their accuracy and efficiency, the developed sphere packing and neutron tracking methods are implemented into an in-house continuous energy Monte Carlo code to solve an eigenvalue problem in VHTR unit cells. Comparison with the MCNP benchmark calculations for the same problem indicates that the new methods show considerably higher computational efficiency. (authors)

Liang, C.; Ji, W. [Dept. of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Inst., 110 8th street, Troy, NY (United States)

2012-07-01

7

Monte Carlo solution for uncertainty propagation in particle transport with a stochastic Galerkin method  

SciTech Connect

The stochastic Galerkin method (SGM) is an intrusive technique for propagating data uncertainty in physical models. The method reduces the random model to a system of coupled deterministic equations for the moments of stochastic spectral expansions of result quantities. We investigate solving these equations using the Monte Carlo technique. We compare the efficiency with brute-force Monte Carlo evaluation of uncertainty, the non-intrusive stochastic collocation method (SCM), and an intrusive Monte Carlo implementation of the stochastic collocation method. We also describe the stability limitations of our SGM implementation. (authors)

Franke, B. C. [Sandia National Laboratories, Albuquerque, NM 87185 (United States); Prinja, A. K. [Department of Chemical and Nuclear Engineering, University of New Mexico, Albuquerque, NM 87131 (United States)

2013-07-01

8

Stochastic molecular dynamics: A combined Monte Carlo and molecular dynamics technique for isothermal simulations  

E-print Network

Stochastic molecular dynamics: A combined Monte Carlo and molecular dynamics technique techniques--Monte Carlo and molecular dynamics--has their own advantage. The molecular dynamics method can to cover the important states of the system in an efficient manner. In recent years the molecular dynamics

Attard, Phil

9

Protein folding and phylogenetic tree reconstruction using stochastic approximation Monte Carlo  

E-print Network

folding problems. The numerical results indicate that it outperforms simulated annealing and conventional Monte Carlo algorithms as a stochastic optimization algorithm. We also propose one method for the use of secondary structures in protein folding...

Cheon, Sooyoung

2007-09-17

10

Divergence of the multilevel Monte Carlo Euler method for nonlinear stochastic differential equations  

Microsoft Academic Search

The Euler-Maruyama scheme is known to diverge strongly and numerically weakly when applied to nonlinear stochastic differential equations (SDEs) with superlinearly growing and globally one-sided Lipschitz continuous drift coefficients. Classical Monte Carlo simulations do, however, not suffer from this divergence behavior of Euler's method because this divergence behavior happens on rare events. Indeed, for such nonlinear SDEs the classical Monte

Martin Hutzenthaler; Arnulf Jentzen; Peter E. Kloeden

2011-01-01

11

A tutorial introduction to Bayesian inference for stochastic epidemic models using Markov chain Monte Carlo methods  

Microsoft Academic Search

Recent Bayesian methods for the analysis of infectious disease outbreak data using stochastic epidemic models are reviewed. These methods rely on Markov chain Monte Carlo methods. Both temporal and non-temporal data are considered. The methods are illustrated with a number of examples featuring different models and datasets.

Philip D. O’Neill

2002-01-01

12

Discrete Time Approximation and Monte-Carlo Simulation of Backward Stochastic Differential Equations  

E-print Network

Discrete Time Approximation and Monte-Carlo Simulation of Backward Stochastic Differential for decoupled forward-backward stochas- tic differential equations. The Lp norm of the error is shown to be of the order of the time step. Given a simulation-based estimator of the conditional expectation operator, we

Université Pierre-et-Marie-Curie, Paris 6

13

Monte Carlo Modeling  

NSDL National Science Digital Library

Monte Carlo modeling refers to the solution of mathematical problems with the use of random numbers. This can include both function integration and the modeling of stochastic phenomena using random processes.

David Joiner

14

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo  

PubMed Central

Computational systems biology is concerned with the development of detailed mechanistic models of biological processes. Such models are often stochastic and analytically intractable, containing uncertain parameters that must be estimated from time course data. In this article, we consider the task of inferring the parameters of a stochastic kinetic model defined as a Markov (jump) process. Inference for the parameters of complex nonlinear multivariate stochastic process models is a challenging problem, but we find here that algorithms based on particle Markov chain Monte Carlo turn out to be a very effective computationally intensive approach to the problem. Approximations to the inferential model based on stochastic differential equations (SDEs) are considered, as well as improvements to the inference scheme that exploit the SDE structure. We apply the methodology to a Lotka–Volterra system and a prokaryotic auto-regulatory network. PMID:23226583

Golightly, Andrew; Wilkinson, Darren J.

2011-01-01

15

Monte Carlo Basics 1 Introduction  

E-print Network

1 Monte Carlo Basics §1 Introduction WHAT IS THE MONTE CARLO METHOD? · Monte Carlo (MC) method. Multidimensional integrations (e.g., statistical mechanics in physics); 2. Simulation of stochastic natural phenomena (e.g., stock price). · Numerical vs. MC Integration The simplest numerical integration of a one

Southern California, University of

16

Monte Carlo tests of stochastic Loewner evolution predictions for the 2D self-avoiding walk.  

PubMed

The conjecture that the scaling limit of the two-dimensional self-avoiding walk (SAW) in a half plane is given by the stochastic Loewner evolution (SLE) with kappa = 8/3 leads to explicit predictions about the SAW. A remarkable feature of these predictions is that they yield not just critical exponents but also probability distributions for certain random variables associated with the self-avoiding walk. We test two of these predictions with Monte Carlo simulations and find excellent agreement, thus providing numerical support to the conjecture that the scaling limit of the SAW is SLE(8/3). PMID:11955086

Kennedy, Tom

2002-04-01

17

Quasi-Monte Carlo Sampling to improve the Efficiency of Monte Carlo EM  

E-print Network

Quasi-Monte Carlo Sampling to improve the Efficiency of Monte Carlo EM Wolfgang Jank Department@rhsmith.umd.edu November 17, 2003 Abstract In this paper we investigate an efficient implementation of the Monte Carlo EM al- gorithm based on Quasi-Monte Carlo sampling. The Monte Carlo EM algorithm is a stochastic version

Jank, Wolfgang

18

Monte Carlo methods Sequential Monte Carlo  

E-print Network

Monte Carlo methods Sequential Monte Carlo A. Doucet Carcans Sept. 2011 A. Doucet (MLSS Sept. 2011) Sequential Monte Carlo Sept. 2011 1 / 85 #12;Generic Problem Consider a sequence of probability distributions, Fn = Fn 1 F. A. Doucet (MLSS Sept. 2011) Sequential Monte Carlo Sept. 2011 2 / 85 #12;Generic Problem

Doucet, Arnaud

19

Stochastic Analysis of Unsaturated Flow: One-Dimensional Monte Carlo Simulations and Comparisons With Spectral Perturbation Analysis and Field Observations  

Microsoft Academic Search

A numerical experiment was designed to study the stochastic behavior of one-dimensional transient unsaturated flow in a Monte Carlo setting. Soil hydraulic properties, log-saturated hydraulic conductivity ln Ks, pore size distribution parameter alpha, and the specific water capacity C are assumed to be statistically homogeneous random fields described by exponential correlation functions with identical correlation lengths. Fifty realizations of each

K. Ünlü; D. R. Nielsen; J. W. Biggar

1990-01-01

20

Stochastic analysis of unsaturated flow: One-dimensional Monte Carlo simulations and comparisons with spectral perturbation analysis and field observations  

Microsoft Academic Search

A numerical experiment was designed to study the stochastic behavior of one-dimensional transient unsaturated flow in a Monte Carlo setting. Soil hydraulic properties, log-saturated hydraulic conductivity ln Ks, pore size distribution parameter ?, and the specific water capacity C are assumed to be statistically homogeneous random fields described by exponential correlation functions with identical correlation lengths. Fifty realizations of each

K. Ünlü; D. R. Nielsen; J. W. Biggar

1990-01-01

21

A stochastic approach to quantum statistics distributions: theoretical derivation and Monte Carlo modelling  

NASA Astrophysics Data System (ADS)

We present a method aimed at a stochastic derivation of the equilibrium distribution of a classical/quantum ideal gas in the framework of the canonical ensemble. The time evolution of these ideal systems is modelled as a series of transitions from one system microstate to another one and thermal equilibrium is reached via a random walk in the single-particle state space. We look at this dynamic process as a Markov chain satisfying the condition of detailed balance and propose a variant of the Monte Carlo Metropolis algorithm able to take into account indistinguishability of identical quantum particles. Simulations performed on different two-dimensional (2D) systems are revealed to be capable of reproducing the correct trends of the distribution functions and other thermodynamic properties. The simulations allow us to show that, away from the thermodynamic limit, a pseudo-Bose-Einstein condensation occurs for a 2D ideal gas of bosons.

Guastella, I.; Bellomonte, L.; Sperandeo-Mineo, R. M.

2009-02-01

22

Combining stochastics and analytics for a fast Monte Carlo decay chain generator  

NASA Astrophysics Data System (ADS)

Various Monte Carlo programs, developed either by small groups or widely available, have been used simulate decays of radioactive chains, from the original parent nucleus to the final stable isotopes. These chains include uranium, thorium, radon, and others, and generally have long-lived parent nuclei. Generating decays within these chains requires a certain amount of computing overhead related to simulating unnecessary decays, time-ordering the final results in post-processing, or both. We present a combination analytic/stochastic algorithm for creating a time-ordered set of decays with position and time correlations, and starting with an arbitrary source age. Thus the simulation costs are greatly reduced, while at the same time avoiding chronological post-processing. We discuss optimization methods within the approach to minimize calculation time, and extension of the algorithm to include various source types.

Kazkaz, K.; Walsh, N.

2011-10-01

23

Can Markov chain Monte Carlo be usefully applied to stochastic processes with hidden birth times?  

NASA Astrophysics Data System (ADS)

This paper examines the power of Markov chain Monte Carlo methods to tackle the `inverse' problem of stochastic population modelling. Namely, given a partial series of event-time observations, believed governed by a known process, what range of model parameters might plausibly explain it? This problem is first introduced in the simple context of an immigration-death process, in which only deaths are recorded, and is then extended through the introduction of birth, standard and power-law logistic growth, and an `odd-even effects' quantum optics model. The results show that simple Metropolis Hastings samplers can be applied to provide useful information on models containing a high degree of complexity. Specific problems highlighted include: the potentially poor mixing qualities of simple Metropolis Hastings samplers; and, that heavily non-symmetric full likelihood surfaces may inflict substantial bias on their associated marginal distributions.

Renshaw, Eric; Gibson, Gavin J.

1998-12-01

24

Graduiertenschule Hybrid Monte Carlo  

E-print Network

Graduiertenschule Hybrid Monte Carlo SS 2005 Heermann - Universit¨at Heidelberg Seite 1 #12;Graduiertenschule · In conventional Monte-Carlo (MC) calculations of condensed matter systems, such as an N probability distribution, unlike Monte-Carlo calculations. · The Hybrid Monte-Carlo (HMC) method combines

Heermann, Dieter W.

25

Stochastic analysis of unsaturated flow: One-dimensional Monte Carlo simulations and comparisons with spectral perturbation analysis and field observations  

Microsoft Academic Search

A numerical experiment was designed to study the stochastic behavior of one-dimensional transient unsaturated flow in a Monte Carlo setting. Soil hydraulic properties, log-saturated hydraulic conductivity 1n K{sub s}, pore size distribution parameter α, and the specific water capacity C are assumed to be statistically homogeneous random fields described by exponential correlation function with identical correlation lengths. Fifty realizations of

Kahraman Unlu; D. R. Neilsen; J. W. Biggar

1990-01-01

26

Monte Carlo simulations of H2 formation on stochastically heated grains  

E-print Network

Continuous-time, random-walk Monte Carlo simulations of H2 formation on grains have been performed for surfaces that are stochastically heated by photons. We have assumed diffuse cloud conditions and used a variety of grains of varying roughness and size based on olivine. The simulations were performed at different optical depths. We confirmed that small grains (r <= 0.02 micron) have low modal temperatures with strong fluctuations, which have a large effect on the efficiency of the formation of molecular hydrogen. The grain size distribution highly favours small grains and therefore H2 formation on these particles makes a large contribution to the overall formation rate for all but the roughest surfaces. We find that at A_V=0 only the roughest surfaces can produce the required amount of molecular hydrogen, but by A_V=1, smoother surfaces are possible alternatives. Use of a larger value for the evaporation energy of atomic hydrogen, but one still consistent with experiment, allows smoother surfaces to produce more H2.

H. M. Cuppen; O. Morata; Eric Herbst

2006-01-24

27

Practical Markov Chain Monte Carlo  

Microsoft Academic Search

Markov chain Monte Carlo using the Metropolis-Hastings algorithm is a general method for the simulation of stochastic processes having probability densities known up to a constant of proportionality. Despite recent advances in its theory, the practice has remained controversial. This article makes the case for basing all inference on one long run of the Markov chain and estimating the Monte

Charles J. Geyer

1992-01-01

28

Fast Monte Carlo Estimation of Timing Yield: Importance Sampling with Stochastic Logical Effort (ISLE)  

E-print Network

In the nano era in integrated circuit fabrication technologies, the performance variability due to statistical process and circuit parameter variations is becoming more and more significant. Considerable effort has been expended in the EDA community during the past several years in trying to cope with the so-called statistical timing problem. Most of this effort has been aimed at generalizing the static timing analyzers to the statistical case. In this paper, we take a pragmatic approach in pursuit of making the Monte Carlo method for timing yield estimation practically feasible. The Monte Carlo method is widely used as a golden reference in assessing the accuracy of other timing yield estimation techniques. However, it is generally believed that it can not be used in practice for estimating timing yield as it requires too many costly full circuit simulations for acceptable accuracy. In this paper, we present a novel approach to constructing an improvedMonte Carlo estimator for timing yield which provides the...

Bayrakci, Alp Arslan; Tasiran, Serdar

2008-01-01

29

Evaluation of Monte Carlo Electron-Transport Algorithms in the Integrated Tiger Series Codes for Stochastic-Media Simulations  

NASA Astrophysics Data System (ADS)

Stochastic-media simulations require numerous boundary crossings. We consider two Monte Carlo electron transport approaches and evaluate accuracy with numerous material boundaries. In the condensed-history method, approximations are made based on infinite-medium solutions for multiple scattering over some track length. Typically, further approximations are employed for material-boundary crossings where infinite-medium solutions become invalid. We have previously explored an alternative "condensed transport" formulation, a Generalized Boltzmann-Fokker-Planck GBFP method, which requires no special boundary treatment but instead uses approximations to the electron-scattering cross sections. Some limited capabilities for analog transport and a GBFP method have been implemented in the Integrated Tiger Series (ITS) codes. Improvements have been made to the condensed history algorithm. The performance of the ITS condensed-history and condensed-transport algorithms are assessed for material-boundary crossings. These assessments are made both by introducing artificial material boundaries and by comparison to analog Monte Carlo simulations.

Franke, Brian C.; Kensek, Ronald P.; Prinja, Anil K.

2014-06-01

30

Stochastic extension of the Lanczos method for nuclear shell-model calculations with variational Monte Carlo method  

E-print Network

We propose a new variational Monte Carlo (VMC) approach based on the Krylov subspace for large-scale shell-model calculations. A random walker in the VMC is formulated with the $M$-scheme representation, and samples a small number of configurations from a whole Hilbert space stochastically. This VMC framework is demonstrated in the shell-model calculations of $^{48}$Cr and $^{60}$Zn, and we discuss its relation to a small number of Lanczos iterations. By utilizing the wave function obtained by the conventional particle-hole-excitation truncation as an initial state, this VMC approach provides us with a sequence of systematically improved results.

Noritaka Shimizu; Takahiro Mizusaki; Kazunari Kaneko

2013-05-09

31

Comparative Monte Carlo efficiency by Monte Carlo analysis  

Microsoft Academic Search

We propose a modified power method for computing the subdominant eigenvalue lambda2 of a matrix or continuous operator. While useful both deterministically and stochastically, we focus on defining simple Monte Carlo methods for its application. The methods presented use random walkers of mixed signs to represent the subdominant eigenfunction. Accordingly, the methods must cancel these signs properly in order to

B. M. Rubenstein; J. E. Gubernatis; J. D. Doll

2010-01-01

32

Sequential Monte Carlo for Model Predictive , J.M. Maciejowski  

E-print Network

Sequential Monte Carlo for Model Predictive Control N. Kantas , J.M. Maciejowski and A. Lecchini, Stochastic MPC, Sequential Monte Carlo Abstract : This paper proposes the use of Sequential Monte Carlo (SMC as expectations over relatively high-dimensional spaces. Monte Carlo methods are currently the most successful

Visintini, Andrea Lecchini

33

Monte Carlo fundamentals  

SciTech Connect

This report is composed of the lecture notes from the first half of a 32-hour graduate-level course on Monte Carlo methods offered at KAPL. These notes, prepared by two of the principle developers of KAPL`s RACER Monte Carlo code, cover the fundamental theory, concepts, and practices for Monte Carlo analysis. In particular, a thorough grounding in the basic fundamentals of Monte Carlo methods is presented, including random number generation, random sampling, the Monte Carlo approach to solving transport problems, computational geometry, collision physics, tallies, and eigenvalue calculations. Furthermore, modern computational algorithms for vector and parallel approaches to Monte Carlo calculations are covered in detail, including fundamental parallel and vector concepts, the event-based algorithm, master/slave schemes, parallel scaling laws, and portability issues.

Brown, F.B.; Sutton, T.M.

1996-02-01

34

Advanced Monte Carlo Methods: Quasi-Monte Carlo  

E-print Network

Advanced Monte Carlo Methods: Quasi-Monte Carlo Prof. Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute QMC ­ p. 1 #12;Quasi Monte Carlo low discrepancy sequences Koksma ­ p. 2 #12;Quasi Monte Carlo Standard Monte Carlo approximates high-dimensional hypercube integral [0

Giles, Mike

35

Markov Chain Monte Carlo Methods Markov Chain Monte Carlo Methods  

E-print Network

Markov Chain Monte Carlo Methods Markov Chain Monte Carlo Methods Christian P. Robert Universit Monte Carlo Methods Textbook: Monte Carlo Statistical Methods by Christian. P. Robert and George Casella Monte Carlo Methods with R by Christian. P. Robert and George Casella [trad. fran¸caise 2010; japonaise

Robert, Christian P.

36

Markov Chain Monte Carlo Methods Markov Chain Monte Carlo Methods  

E-print Network

Markov Chain Monte Carlo Methods Markov Chain Monte Carlo Methods Christian P. Robert Universit Monte Carlo Methods Outline Motivation and leading example Random variable generation Monte Carlo for variable dimension problems Sequential importance sampling #12;Markov Chain Monte Carlo Methods New [2004

Robert, Christian P.

37

STP Monte Carlo Estimation  

NSDL National Science Digital Library

The STP MonteCarloEstimation program estimates the area under the curve given by the square-root of (1-x^2) between 0 and 1 using the Monte Carlo hit and miss method. STP MonteCarloEstimation is part of a suite of Open Source Physics programs that model aspects of Statistical and Thermal Physics (STP). The program is distributed as a ready-to-run (compiled) Java archive. Double clicking the stp_MonteCarloEstimation.jar file will run the program if Java is installed on your computer.

Gould, Harvey; Tobochnik, Jan; Christian, Wolfgang; Cox, Anne

2009-01-26

38

Monte Carlo method based radiative transfer simulation of stochastic open forest generated by circle packing application  

NASA Astrophysics Data System (ADS)

Monte Carlo Ray Tracing (MCRT) method is a versatile application for simulating radiative transfer regime of the Solar - Atmosphere - Landscape system. Moreover, it can be used to compute the radiation distribution over a complex landscape configuration, as an example like a forest area. Due to its robustness to the complexity of the 3-D scene altering, MCRT method is also employed for simulating canopy radiative transfer regime as the validation source of other radiative transfer models. In MCRT modeling within vegetation, one basic step is the canopy scene set up. 3-D scanning application was used for representing canopy structure as accurately as possible, but it is time consuming. Botanical growth function can be used to model the single tree growth, but cannot be used to express the impaction among trees. L-System is also a functional controlled tree growth simulation model, but it costs large computing memory. Additionally, it only models the current tree patterns rather than tree growth during we simulate the radiative transfer regime. Therefore, it is much more constructive to use regular solid pattern like ellipsoidal, cone, cylinder etc. to indicate single canopy. Considering the allelopathy phenomenon in some open forest optical images, each tree in its own `domain' repels other trees. According to this assumption a stochastic circle packing algorithm is developed to generate the 3-D canopy scene in this study. The canopy coverage (%) and the tree amount (N) of the 3-D scene are declared at first, similar to the random open forest image. Accordingly, we randomly generate each canopy radius (rc). Then we set the circle central coordinate on XY-plane as well as to keep circles separate from each other by the circle packing algorithm. To model the individual tree, we employ the Ishikawa's tree growth regressive model to set the tree parameters including DBH (dt), tree height (H). However, the relationship between canopy height (Hc) and trunk height (Ht) is unclear to us. We assume the proportion between Hc and Ht as a random number in the interval from 2.0 to 3.0. De Wit's sphere leaf angle distribution function was used within the canopy for acceleration. Finally, we simulate the open forest albedo using MCRT method. The MCRT algorithm of this study is summarized as follows (1) Initialize the photon with a position (r0), source direction (?0) and intensity (I0), respectively. (2) Simulate the free path (s) of a photon under the condition of (r', ?, I') in the canopy. (3) Calculate the new position of the photon r=r +s?'. (4) Determine the new scattering direction (?)after collision at, r and then calculate the new intensity I = ?L(?L,?'-->?)I'.(5) Accumulate the intensity I of a photon escaping from the top boundary of the 3-D Scene, otherwise redo from step (2), until I is smaller than a threshold. (6) Repeat from step (1), for each photon. We testify the model on four different simulated open forests and the effectiveness of the model is demonstrated in details.

Jin, Shengye; Tamura, Masayuki

2013-10-01

39

Multigrid Monte Carlo method. Conceptual foundations  

Microsoft Academic Search

We present details of a stochastic generalization of the multigrid method, called multigrid Monte Carlo (MGMC), that reduces critical slowing down in Monte Carlo computations of lattice field theories. For Gaussian (free) fields, critical slowing down is completely eliminated. For a phi4 model, numerical experiments show a factor of ~=10 reduction, over a standard heat-bath algorithm, in the CPU time

Jonathan Goodman; Alan D. Sokal

1989-01-01

40

A Diffusion Monte Carlo Simulation of Quantum Dot hetero-structures using a Stochastic Poisson Solver  

NASA Astrophysics Data System (ADS)

Quantum Monte Carlo (QMC) is an extremely powerful method to to treat many-body systems. Usually QMC has been applied in cases where the interaction has a simple analytic form like the Coulomb potential. However, in a complicated environment, as in a semiconductor heterostructure, the evaluation of the interaction itself becomes a non-trivial problem. Solving for which by any grid based method, for every walker and iteration step is unfeasible. We circumvent this problem by solving the Poisson's Equation by a classical Monte Carlo within the overall QMC scheme. We have developed a modified ``Walk on Spheres'' algorithm using Green's function techniques, which can efficiently handle planar dielectric interfaces typical of actual hetero-structures. Moreover, if the quantum electronic system is clustered together, as in a quantum dot, then the same walk can sample the potential for the entire cluster. Since this additional process increases the already large dimensionality of the problem only slightly (namely by three), a coarse estimate of the potential for a specific walker configuration is sufficient. We apply this method to study the ground state properties of a quantum dot in the presence of applied gate potentials. DOE DEFG02-91ER4543, NSF DMR99-76550

Dyutiman, Das; Martin, Richard; Kalos, Malvin

2003-03-01

41

Symbolic Implicit Monte Carlo  

Microsoft Academic Search

We introduce a new implicit Monte Carlo technique for solving time dependent radiation transport problems involving spontaneous emission. In the usual implicit Monte Carlo procedure an effective scattering term in dictated by the requirement of self-consistency between the transport and implicitly differenced atomic populations equations. The effective scattering term, a source of inefficiency for optically thick problems, becomes an impasse

Eugene D. Brooks III

1989-01-01

42

Monte Carlo POMDPs Sebastian Thrun  

E-print Network

Monte Carlo POMDPs Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a Monte Carlo algorithm for learning to act in partially observable sampling forrepresentingbeliefs, and Monte Carlo approximation for belief propagation. A reinforcement

Thrun, Sebastian

43

The non-Markovian quantum behavior of open systems: An exact Monte Carlo method employing stochastic product states  

E-print Network

It is shown that the exact dynamics of a composite quantum system can be represented through a pair of product states which evolve according to a Markovian random jump process. This representation is used to design a general Monte Carlo wave function method that enables the stochastic treatment of the full non-Markovian behavior of open quantum systems. Numerical simulations are carried out which demonstrate that the method is applicable to open systems strongly coupled to a bosonic reservoir, as well as to the interaction with a spin bath. Full details of the simulation algorithms are given, together with an investigation of the dynamics of fluctuations. Several potential generalizations of the method are outlined.

Heinz-Peter Breuer

2003-09-15

44

MORSE Monte Carlo code  

SciTech Connect

The MORSE code is a large general-use multigroup Monte Carlo code system. Although no claims can be made regarding its superiority in either theoretical details or Monte Carlo techniques, MORSE has been, since its inception at ORNL in the late 1960s, the most widely used Monte Carlo radiation transport code. The principal reason for this popularity is that MORSE is relatively easy to use, independent of any installation or distribution center, and it can be easily customized to fit almost any specific need. Features of the MORSE code are described.

Cramer, S.N.

1984-01-01

45

34. Monte Carlo techniques 1 34. MONTE CARLO TECHNIQUES  

E-print Network

34. Monte Carlo techniques 1 34. MONTE CARLO TECHNIQUES Revised September 2011 by G. Cowan (RHUL). Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample distribution Most Monte Carlo sampling or integration techniques assume a "random number generator," which

46

34. Monte Carlo techniques 1 34. MONTE CARLO TECHNIQUES  

E-print Network

34. Monte Carlo techniques 1 34. MONTE CARLO TECHNIQUES Revised September 2009 by G. Cowan (RHUL). Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample distribution Most Monte Carlo sampling or integration techniques assume a "random number generator," which

47

Secondproofs Monte Carlo and Quasi-Monte Carlo Methods 2008  

E-print Network

Secondproofs Monte Carlo and Quasi-Monte Carlo Methods 2008 #12;Secondproofs #12;Secondproofs Pierre L'Ecuyer r Art B. Owen Editors Monte Carlo and Quasi-Monte Carlo Methods 2008 #12;Secondproofs, CA 94305 USA owen@stanford.edu ISBN 978-3-642-04106-8 DOI 10.1007/978-3-642-04107-5 e-ISBN978

L'Ecuyer, Pierre

48

37. Monte Carlo techniques 1 37. MONTE CARLO TECHNIQUES  

E-print Network

37. Monte Carlo techniques 1 37. MONTE CARLO TECHNIQUES Revised September 2011 by G. Cowan (RHUL). Monte Carlo techniques are often the only practical way to evaluate difficult integrals or to sample distribution Most Monte Carlo sampling or integration techniques assume a "random number generator," which

49

Monte Carlo methods Monte Carlo Principle and MCMC  

E-print Network

Monte Carlo methods Monte Carlo Principle and MCMC A. Doucet Carcans Sept. 2011 A. Doucet (MLSS Sept. 2011) MCMC Sept. 2011 1 / 91 #12;Overview of the Lectures 1 Monte Carlo Principles A. Doucet (MLSS Sept. 2011) MCMC Sept. 2011 2 / 91 #12;Overview of the Lectures 1 Monte Carlo Principles 2 Markov

Doucet, Arnaud

50

The role of Monte Carlo within a diagonalization/Monte Carlo scheme  

E-print Network

We review the method of stochastic error correction which eliminates the truncation error associated with any subspace diagonalization. Monte Carlo sampling is used to compute the contribution of the remaining basis vectors not included in the initial diagonalization. The method is part of a new approach to computational quantum physics which combines both diagonalization and Monte Carlo techniques.

Dean Lee

2000-10-31

51

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations  

E-print Network

Parallel Hybrid Monte Carlo Algorithms for Matrix Computations V. Alexandrov1 , E. Atanassov2 , I Equations (SLAE). Monte Carlo meth- ods are used for the stochastic approximation, since it is known experimental results are presented. Keywords: Monte Carlo Method, Markov Chain, Matrix Inversion, So- lution

Dimov, Ivan

52

Quantum Monte Carlo Calculations for Minimum Energy Structures  

E-print Network

We present an efficient method to find minimum energy structures using energy estimates from accurate quantum Monte Carlo calculations. This method involves a stochastic process formed from the stochastic energy estimates ...

Grossman, Jeffrey C.

53

Error in Monte Carlo, quasi-error in Quasi-Monte Carlo  

E-print Network

While the Quasi-Monte Carlo method of numerical integration achieves smaller integration error than standard Monte Carlo, its use in particle physics phenomenology has been hindered by the abscence of a reliable way to estimate that error. The standard Monte Carlo error estimator relies on the assumption that the points are generated independently of each other and, therefore, fails to account for the error improvement advertised by the Quasi-Monte Carlo method. We advocate the construction of an estimator of stochastic nature, based on the ensemble of pointsets with a particular discrepancy value. We investigate the consequences of this choice and give some first empirical results on the suggested estimators.

R. H. Kleiss; A. Lazopoulos

2005-04-12

54

Hypervolume Monte Carlo Model  

NSDL National Science Digital Library

The Hypervolume Monte Carlo Model implements microcanonical simulations by sampling the position and momentum spaces. Although it is strictly proven in the thermodynamic limit, HVMC works well with a relatively small number of molecules. In contrast to other algorithms for Monte Carlo simulations, HVMC does not involve previous integration over the momentum space or demons. It is the full non-deterministic counter part of the NVE molecular dynamics method, also providing speed distribution functions. Moreover, the method allows a straightforward simulation of the ideal gas. The Hypervolume Monte Carlo Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.

2012-12-04

55

Monte Carlo Methods for Statistical Inference  

Microsoft Academic Search

\\u000a Monte Carlo methods are experiments. Monte Carlo experimentation is the use of simulated random numbers to estimate some functional of a probability distribution.\\u000a A problem that does not have a stochastic component can sometimes be posed as a problem with a component that can be identified\\u000a with an expectation of some function of a random variable. This is often done

James E. Gentle

56

Sequential Monte Carlo pricing of American-style options under stochastic volatility models  

Microsoft Academic Search

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be expressed as functions of conditional distributions of volatility, given observed data. By constructing

Bhojnarine R. Rambharat; Anthony E. Brockwell

2010-01-01

57

Monte Carlo Neutrino Oscillations  

E-print Network

We demonstrate that the effects of matter upon neutrino propagation may be recast as the scattering of the initial neutrino wavefunction. Exchanging the differential, Schrodinger equation for an integral equation for the scattering matrix S permits a Monte Carlo method for the computation of S that removes many of the numerical difficulties associated with direct integration techniques.

James P. Kneller; Gail C. McLaughlin

2005-09-29

58

Baseball Monte Carlo Style.  

ERIC Educational Resources Information Center

Monte Carlo methods are used to simulate activities in baseball such as a team's "hot streak" and a hitter's "batting slump." Student participation in such simulations is viewed as a useful method of giving pupils a better understanding of the probability concepts involved. (MP)

Houser, Larry L.

1981-01-01

59

Monte Carlo POMDPs Sebastian Thrun  

E-print Network

Monte Carlo POMDPs Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Abstract We present a Monte Carlo algorithm for learning to act in partially observable sampling for representing beliefs, and Monte Carlo approximation for belief propagation. A reinforcement

Thrun, Sebastian

60

Monte Carlo methods Rmi Bardenet  

E-print Network

Monte Carlo methods Rémi Bardenet 1 Department of Statistics, Oxford University Abstract. Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo they are not analytically tractable. We review here the basic principles and the most common Monte Carlo algorithms, among

Boyer, Edmond

61

Quantum Monte Carlo Helsinki 2011  

E-print Network

Quantum Monte Carlo Helsinki 2011 Marius Lewerenz MSME/CT, UMR 8208 CNRS, Universit´e Paris Est? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 What is a Monte Carlo method? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 What are Monte Carlo methods good for? . . . . . . . . . . . . . . . . . . . . . . . 5 1

Boyer, Edmond

62

Monte-Carlo Tests Diplomarbeit  

E-print Network

Monte-Carlo Tests Diplomarbeit Wiebke Werft Mathematisches Institut der Heinrich.2 Suffizienz und Vollständigkeit . . . . . . . . . . . . . . . . . . . . 5 2 Monte-Carlo Tests 8 2.1 Formulierung des Testproblems . . . . . . . . . . . . . . . . . . . 8 2.2 Definition des Monte-Carlo Tests

63

Monte Carlo techniques for real-time quantum dynamics  

Microsoft Academic Search

The stochastic-gauge representation is a method of mapping the equation of motion for the quantum mechanical density operator onto a set of equivalent stochastic differential equations. One of the stochastic variables is termed the “weight”, and its magnitude is related to the importance of the stochastic trajectory. We investigate the use of Monte Carlo algorithms to improve the sampling of

Mark R.. Dowling; Matthew J. Davis; Peter D. Drummond; Joel F. Corney

2007-01-01

64

Monte Carlo method in optical radiometry  

Microsoft Academic Search

State-of-the-art in the application of the Monte Carlo method (MCM) to the computational problems of optical radiometry is discussed. The MCM offers a universal technique for radiation transfer modelling based on the stochastic approach. Developments of the original MCM algorithms and software for calculation of effective emissivities of black bodies, absorption characteristics of cavity radiometers and photometric properties of integrating

A. V. Prokhorov

1998-01-01

65

Monte-Carlo simulation of primary stochastic effects induced at the cellular level in boron neutron capture therapy  

Microsoft Academic Search

A Monte Carlo code is developed to study the action of particles in Boron Neutron Capture Therapy (BNCT). Our aim is to calculate the probability of dissipating a lethal dose in cell nuclei. Cytoplasmic and nuclear membranes are considered as non-concentric ellipsoids. All geometrical parameters may be adjusted to fit actual configurations. The reactions 10B(n,gammaalpha)^7Li and 14N(n,p)14C create heavy ions

L. Cirioni; J. P. Patau; F. Nepveu

1998-01-01

66

Quantum Gibbs ensemble Monte Carlo  

E-print Network

We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs ensemble Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs ensemble Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of $^4$He in two dimensions.

Riccardo Fantoni; Saverio Moroni

2014-08-24

67

Advanced Monte Carlo Methods: General Principles of the Monte  

E-print Network

Advanced Monte Carlo Methods: General Principles of the Monte Carlo Method Prof. Dr. Michael of Monte CarloProf. Dr. Michael Mascagni: Advanced Monte Carlo Methods Slide 2 of 61 Numerical Integration: The Canonical Monte Carlo Application Numerical integration is a simple problem to explain and thoroughly

Mascagni, Michael

68

MONTE CARLO EXTENSION OF QUASIMONTE CARLO Art B. Owen  

E-print Network

MONTE CARLO EXTENSION OF QUASI­MONTE CARLO Art B. Owen Department of Statistics Stanford University Stanford CA 94305, U.S.A. ABSTRACT This paper surveys recent research on using Monte Carlo techniques to improve quasi­Monte Carlo tech­ niques. Randomized quasi­Monte Carlo methods pro­ vide a basis for error

Owen, Art

69

A Monte Carlo method for thermal building simulation  

Microsoft Academic Search

A simplified Monte Carlo method for finding an approximation of the building inside temperature distribution is given. Present simulation techniques are either over-simplified and use only a deterministic method, or are highly complex stochastic models. The new method consists of a Monte Carlo approach to find typical input distributions, used in conjunction with a more traditional deterministic building thermal simulation

J. Haarhoff; E. H. Mathews

2006-01-01

70

Monte Carlo and Quasi-Monte Carlo algorithms for the Barker-Ferry equation with low  

E-print Network

Monte Carlo and Quasi-Monte Carlo algorithms for the Barker-Ferry equation with low complexity ? T. The quasi-Monte Carlo (QMC) solutions obtained by QRNs are compared with the Monte Carlo (MC) solutions) converges [3] and the solution can be evaluated by a MC estimator. 2 Monte Carlo and Quasi-Monte Carlo

Whitlock, Paula

71

Systems for Monte Carlo work  

Microsoft Academic Search

With the proliferation of computers has come a proliferation of simulation. Monte Carlo experiments can now be run by a vast range of programs from simple. Basic environments to spreadsheets: yet little attention has been paid to the problem of designing a system to do Monte Carlo problems. The ideas for a system described in this paper not only simplifies

David Alan Grier

1987-01-01

72

MCMini: Monte Carlo on GPGPU  

SciTech Connect

MCMini is a proof of concept that demonstrates the possibility for Monte Carlo neutron transport using OpenCL with a focus on performance. This implementation, written in C, shows that tracing particles and calculating reactions on a 3D mesh can be done in a highly scalable fashion. These results demonstrate a potential path forward for MCNP or other Monte Carlo codes.

Marcus, Ryan C. [Los Alamos National Laboratory

2012-07-25

73

Monte Carlo Studies of Surface Chemistry  

NASA Astrophysics Data System (ADS)

Water is the major constituent of the icy mantles found on interstellar grains. In this contribution, the rate of its production is calculated using the continuous-time random-walk Monte Carlo simulation technique. The visual extinction, density and gas and grain temperature are varied. It is shown that our stochastic approach can reproduce the important observation that ice mantles only grow in the denser regions. This work sill appear in detail in Cuppen & Herbst (2007).

Cuppen, Herma; Herbst, Eric

2007-12-01

74

Quantum Gibbs ensemble Monte Carlo.  

PubMed

We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs ensemble Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs ensemble Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of (4)He in two dimensions. PMID:25240348

Fantoni, Riccardo; Moroni, Saverio

2014-09-21

75

Convergence of the variational parameter without convergence of the energy in Quantum Monte Carlo (QMC) calculations using the Stochastic Gradient Approximation  

NASA Astrophysics Data System (ADS)

To study the performance of the Stochastic Gradient Approximation (SGA) for variational Quantum Monte Carlo methods, we have considered lithium nano-clusters [1] described by Hartree-Fock wavefunctions multiplied by two-body Jastrow factors with a single variational parameter b. Even when the system size increases, we have shown the feasibility of obtaining an accurate value of b that minimizes the energy without an explicit calculation of the energy itself. The present SGA algorithm is so efficient because an analytic gradient formula is used and because the statistical noise in the gradient is smaller than in the energy [2]. Interestingly, in this scheme the absolute value of the gradient is less important than the sign of the gradient. Work supported in part by U.S. DOE. [1] D. Nissenbaum et al., Phys. Rev. B 76, 033412 (2007). [2] A. Harju, J. Low. Temp. Phys. 140, 181 (2005).

Nissenbaum, Daniel; Lin, Hsin; Barbiellini, Bernardo; Bansil, Arun

2009-03-01

76

ORIE 5582: Monte Carlo Methods in Financial Engineering This course covers the principles of derivative pricing, generation of sample paths and  

E-print Network

ORIE 5582: Monte Carlo Methods in Financial Engineering This course covers the principles, 2009 Prerequisites ORIE 5581 (Monte Carlo Simulation) ORIE 5600 (Stochastic calculus) Instructor Peter books may prove helpful. Monte Carlo Methods in Financial Engineering. P. Glasserman. Springer

Keinan, Alon

77

Advanced Monte Carlo Aiichiro Nakano  

E-print Network

Advanced Monte Carlo Aiichiro Nakano Collaboratory for Advanced Computing & Simulations Department. Lett. 100, 163103 ('12) #12;Multicanonical Ensemble Energy Boltzmannfactor Configuration space M E( ) = D E( )PM E( ) = constant 1-dimensional random walk in energy space: fast Multicanonical MC algorithm

Southern California, University of

78

THE MONTE CARLO METHOD I. INTRODUCTION  

E-print Network

THE MONTE CARLO METHOD I. INTRODUCTION The Monte Carlo method is often referred to as a `computer physics. The purpose of this note is partly to emphasize some of the mathematical rigor behind Monte Carlo complicated for analytic techniques. With all that said, it is still useful to pursue the `Monte Carlo

California at Davis, University of

79

A system for Monte Carlo experimentation  

Microsoft Academic Search

A new computer system for Monte Carlo Experimentation is presented in this thesis. The new system speeds and simplifies the process of coding and preparing a Monte Carlo Experiment; it also encourages the proper design of Monte Carlo Experiments, and the careful analysis of the experimental results.A new functional language is the core of this system. Monte Carlo Experiments, and

David Alan Grier

1986-01-01

80

Computer system for Monte Carlo experimentation  

Microsoft Academic Search

A new computer system for Monte Carlo Experimentation is presented. The new system speeds and simplifies the process of coding and preparing a Monte Carlo Experiment; it also encourages the proper design of Monte Carlo Experiments, and the careful analysis of the experimental results. A new functional language is the core of this system. Monte Carlo Experiments, and their experimental

Grier

1986-01-01

81

Monte Carlo Methods for Inference and Learning  

E-print Network

Monte Carlo Methods for Inference and Learning Guest Lecturer: Ryan Adams CSC 2535 http://www.cs.toronto.edu/~rpa #12;Overview ·Monte Carlo basics ·Rejection and Importance sampling ·Markov chain Monte Carlo ·Metropolis-Hastings and Gibbs sampling ·Slice sampling ·Hamiltonian Monte Carlo #12;Computing Expectations We

Hinton, Geoffrey E.

82

Monto Carlo extension of quasi-Monte Carlo  

Microsoft Academic Search

This paper surveys recent research on using Monte Carlo techniques to improve quasi-Monte Carlo techniques. Randomized quasi-Monte Carlo methods provide a basis for error estimation. They have, in the special case of scrambled nets, also been observed to improve accuracy. Finally through Latin supercube sampling it is possible to use Monte Carlo methods to extend quasi-Monte Carlo methods to higher

Art B. Owen

1998-01-01

83

Monte Carlo and Quasi-Monte Carlo for Art B. Owen  

E-print Network

Monte Carlo and Quasi-Monte Carlo for Statistics Art B. Owen Abstract This article reports Monte Carlo methods can be used. There was a special emphasis on areas where Quasi-Monte Carlo ideas This survey is aimed at exposing good problems in statistics to researchers in Quasi- Monte Carlo. It has

Owen, Art

84

Limit theorems for weighted samples with applications to sequential Monte Carlo methods  

Microsoft Academic Search

In the last decade, sequential Monte Carlo methods (SMC) emerged as a key tool in computational statistics [see, e.g., Sequential Monte Carlo Methods in Practice (2001) Springer, New York, Monte Carlo Strategies in Scientific Computing (2001) Springer, New York, Complex Stochastic Systems (2001) 109–173]. These algorithms approximate a sequence of distributions by a sequence of weighted empirical measures associated to

Randal Douc; Eric Moulines

2008-01-01

85

Monte Carlo Methods for Computation and Optimization (048715) Winter 2013/4  

E-print Network

Monte Carlo Methods for Computation and Optimization (048715) Winter 2013/4 Lecture Notes Nahum Shimkin i #12;PREFACE These lecture notes are intended for a first, graduate-level, course on Monte-Carlo, Simulation and the Monte Carlo Method, Wiley, 2008. (2) S. Asmussen and P. Glynn, Stochastic Simulation

Shimkin, Nahum

86

Quantum Monte Carlo simulation of thin magnetic films P. Henelius,1,  

E-print Network

Quantum Monte Carlo simulation of thin magnetic films P. Henelius,1, * P. Fro¨brich,2,3 P. J. Kuntz Received 30 April 2002; published 6 September 2002 The stochastic series expansion quantum Monte Carlo theoretical approaches above differ significantly from each other, and the Monte Carlo method is free

von Oppen, Felix

87

3D visualisation of the stochastic patterns of the radial dose in nano-volumes by a Monte Carlo simulation of HZE ion track structure.  

PubMed

The description of energy deposition by high charge and energy (HZE) nuclei is of importance for space radiation risk assessment and due to their use in hadrontherapy. Such ions deposit a large fraction of their energy within the so-called core of the track and a smaller proportion in the penumbra (or track periphery). We study the stochastic patterns of the radial dependence of energy deposition using Monte Carlo track structure codes RITRACKS and RETRACKS, that were used to simulate HZE tracks and calculate energy deposition in voxels of 40 nm. The simulation of a (56)Fe(26+) ion of 1 GeV u(-1) revealed zones of high-energy deposition which maybe found as far as a few millimetres away from the track core in some simulations. The calculation also showed that ?43 % of the energy was deposited in the penumbra. These 3D stochastic simulations combined with a visualisation interface are a powerful tool for biophysicists which may be used to study radiation-induced biological effects such as double strand breaks and oxidative damage and the subsequent cellular and tissue damage processing and signalling. PMID:21199826

Plante, Ianik; Ponomarev, Artem; Cucinotta, Francis A

2011-02-01

88

Application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) to the steel process chain: case study.  

PubMed

The purpose of the paper is to present the results of application of stochastic approach based on Monte Carlo (MC) simulation for life cycle inventory (LCI) data of Mittal Steel Poland (MSP) complex in Kraków, Poland. In order to assess the uncertainty, the software CrystalBall® (CB), which is associated with Microsoft® Excel spreadsheet model, is used. The framework of the study was originally carried out for 2005. The total production of steel, coke, pig iron, sinter, slabs from continuous steel casting (CSC), sheets from hot rolling mill (HRM) and blast furnace gas, collected in 2005 from MSP was analyzed and used for MC simulation of the LCI model. In order to describe random nature of all main products used in this study, normal distribution has been applied. The results of the simulation (10,000 trials) performed with the use of CB consist of frequency charts and statistical reports. The results of this study can be used as the first step in performing a full LCA analysis in the steel industry. Further, it is concluded that the stochastic approach is a powerful method for quantifying parameter uncertainty in LCA/LCI studies and it can be applied to any steel industry. The results obtained from this study can help practitioners and decision-makers in the steel production management. PMID:24290145

Bieda, Bogus?aw

2014-05-15

89

Monte Carlo Simulation of Interacting Electron Models  

E-print Network

Monte Carlo Simulation of Interacting Electron Models by a New Determinant Approach by Mucheng discusses the calculation of determinants and Monte Carlo simulation of Hub- bard models by a new and a Monte Carlo summation algorithm to evaluate the relevant diagram determinant sums. Index words: Monte

Robinson, Robert W.

90

Hard Spheres Monte Carlo Model  

NSDL National Science Digital Library

The Hard Sphere Monte Carlo Model performs canonical Monte Carlo simulations of 256 or 500 hard spheres covering the fluid and solid states. The results are analysed through the radial distributions functions from which the equation of state (EOS) is estimated. This is done by fitting a polynomial to the radial distribution functions in order to exrapolate them to the hard spheres distance of contact. The consistency of the simulations is assessed by the errors of the predicted compressibility factors relatively to the accurate EOS reported by Wu and Sadus. The Hard Sphere Monte Carlo Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.; Freitas, Filomena

2013-02-20

91

Proton Upset Monte Carlo Simulation  

NASA Technical Reports Server (NTRS)

The Proton Upset Monte Carlo Simulation (PROPSET) program calculates the frequency of on-orbit upsets in computer chips (for given orbits such as Low Earth Orbit, Lunar Orbit, and the like) from proton bombardment based on the results of heavy ion testing alone. The software simulates the bombardment of modern microelectronic components (computer chips) with high-energy (.200 MeV) protons. The nuclear interaction of the proton with the silicon of the chip is modeled and nuclear fragments from this interaction are tracked using Monte Carlo techniques to produce statistically accurate predictions.

O'Neill, Patrick M.; Kouba, Coy K.; Foster, Charles C.

2009-01-01

92

First principles quantum Monte Carlo  

E-print Network

Present quantum Monte Carlo codes use statistical techniques adapted to find the amplitude of a quantum system or the associated eigenvalues. Thus, they do not use a true physical random source. It is demonstrated that, in fact, quantum probability admits a description based on a specific class of random process at least for the single particle case. Then a first principle Monte Carlo code that exactly simulates quantum dynamics can be constructed. The subtle question concerning how to map random choices in amplitude interferences is explained. Possible advantages of this code in simulating single hit experiments are discussed.

J. M. A. Figueiredo

2006-12-07

93

A Monte Carlo approach to water management  

NASA Astrophysics Data System (ADS)

Common methods for making optimal decisions in water management problems are insufficient. Linear programming methods are inappropriate because hydrosystems are nonlinear with respect to their dynamics, operation constraints and objectives. Dynamic programming methods are inappropriate because water management problems cannot be divided into sequential stages. Also, these deterministic methods cannot properly deal with the uncertainty of future conditions (inflows, demands, etc.). Even stochastic extensions of these methods (e.g. linear-quadratic-Gaussian control) necessitate such drastic oversimplifications of hydrosystems that may make the obtained results irrelevant to the real world problems. However, a Monte Carlo approach is feasible and can form a general methodology applicable to any type of hydrosystem. This methodology uses stochastic simulation to generate system inputs, either unconditional or conditioned on a prediction, if available, and represents the operation of the entire system through a simulation model as faithful as possible, without demanding a specific mathematical form that would imply oversimplifications. Such representation fully respects the physical constraints, while at the same time it evaluates the system operation constraints and objectives in probabilistic terms, and derives their distribution functions and statistics through Monte Carlo simulation. As the performance criteria of a hydrosystem operation will generally be highly nonlinear and highly nonconvex functions of the control variables, a second Monte Carlo procedure, implementing stochastic optimization, is necessary to optimize system performance and evaluate the control variables of the system. The latter is facilitated if the entire representation is parsimonious, i.e. if the number of control variables is kept at a minimum by involving a suitable system parameterization. The approach is illustrated through three examples for (a) a hypothetical system of two reservoirs performing a variety of functions, (b) the water resource system of Athens comprising four reservoirs and many aqueducts, and (c) a human-modified inadequately measured basin in which the parameter fitting of a hydrological model is sought.

Koutsoyiannis, D.

2012-04-01

94

Monte Carlo Simulation of Quantum Computation  

E-print Network

The many-body dynamics of a quantum computer can be reduced to the time evolution of non-interacting quantum bits in auxiliary fields by use of the Hubbard-Stratonovich representation of two-bit quantum gates in terms of one-bit gates. This makes it possible to perform the stochastic simulation of a quantum algorithm, based on the Monte Carlo evaluation of an integral of dimension polynomial in the number of quantum bits. As an example, the simulation of the quantum circuit for the Fast Fourier Transform is discussed.

N. J. Cerf; S. E. Koonin

1997-03-26

95

Monte Carlo Stratego Jeroen Mets  

E-print Network

Monte Carlo Stratego Jeroen Mets 27 juni 2008 1 Inleiding Stratego staat vooral bekend als een ieder 40 stukken, ge¨inspireerd door 18e-eeuwse legereenheden, wordt er een veldslag nagebootst. Het stellen, mits er op een veld niet meer dan ´e´en stuk wordt geplaatst. In Figuur 2 is een

Emmerich, Michael

96

Monte Carlo simulation for IRRMA  

Microsoft Academic Search

Monte Carlo simulation is fast becoming a standard approach for many radiation applications that were previously treated almost entirely by experimental techniques. This is certainly true for Industrial Radiation and Radioisotope Measurement Applications — IRRMA. The reasons for this include: (1) the increased cost and inadequacy of experimentation for design and interpretation purposes; (2) the availability of low cost, large

Robin P. Gardner; Lianyan Liu

2000-01-01

97

Adjoint electron Monte Carlo calculations  

Microsoft Academic Search

Adjoint Monte Carlo is the most efficient method for accurate analysis of space systems exposed to natural and artificially enhanced electron environments. Recent adjoint calculations for isotropic electron environments include: comparative data for experimental measurements on electronics boxes; benchmark problem solutions for comparing total dose prediction methodologies; preliminary assessment of sectoring methods used during space system design; and total dose

1986-01-01

98

Synchronous Parallel Kinetic Monte Carlo  

SciTech Connect

A novel parallel kinetic Monte Carlo (kMC) algorithm formulated on the basis of perfect time synchronicity is presented. The algorithm provides an exact generalization of any standard serial kMC model and is trivially implemented in parallel architectures. We demonstrate the mathematical validity and parallel performance of the method by solving several well-understood problems in diffusion.

Mart?nez, E; Marian, J; Kalos, M H

2006-12-14

99

EDDE Monte Carlo event generator  

E-print Network

EDDE is a Monte Carlo event generator, under construction, for different Exclusive Double Diffractive Events. The program is based on the extended Regge-eikonal approach for "soft" processes. Standard Model and its extensions are used for "hard" fusion processes. An interface to PYTHIA, CMSJET and CMKIN is provided.

V. A. Petrov; R. A. Ryutin; A. E. Sobol; J. -P. Guillaud

2005-09-26

100

Monte Carlo for the LHC  

E-print Network

I review the status of the general-purpose Monte Carlo event generators for the LHC, with emphasis on areas of recent physics developments. There has been great progress, especially in multi-jet simulation, but I mention some question marks that have recently arisen.

Michael H. Seymour

2010-08-17

101

Is Monte Carlo embarrassingly parallel?  

SciTech Connect

Monte Carlo is often stated as being embarrassingly parallel. However, running a Monte Carlo calculation, especially a reactor criticality calculation, in parallel using tens of processors shows a serious limitation in speedup and the execution time may even increase beyond a certain number of processors. In this paper the main causes of the loss of efficiency when using many processors are analyzed using a simple Monte Carlo program for criticality. The basic mechanism for parallel execution is MPI. One of the bottlenecks turn out to be the rendez-vous points in the parallel calculation used for synchronization and exchange of data between processors. This happens at least at the end of each cycle for fission source generation in order to collect the full fission source distribution for the next cycle and to estimate the effective multiplication factor, which is not only part of the requested results, but also input to the next cycle for population control. Basic improvements to overcome this limitation are suggested and tested. Also other time losses in the parallel calculation are identified. Moreover, the threading mechanism, which allows the parallel execution of tasks based on shared memory using OpenMP, is analyzed in detail. Recommendations are given to get the maximum efficiency out of a parallel Monte Carlo calculation. (authors)

Hoogenboom, J. E. [Delft Univ. of Technology, Mekelweg 15, 2629 JB Delft (Netherlands); Delft Nuclear Consultancy, IJsselzoom 2, 2902 LB Capelle aan den IJssel (Netherlands)

2012-07-01

102

Jet evolution and Monte Carlo  

E-print Network

In this lecture I discuss jet-shape distributions and describe how from jet evolution one may design Monte Carlo simulations which are used in the analysis of short distance distributions in $\\ee$-annihilation, lepton-hadron and hadron-hadron collisions

Giuseppe Marchesini

2005-01-24

103

Monte Carlo techniques for real-time quantum dynamics  

E-print Network

The stochastic-gauge representation is a method of mapping the equation of motion for the quantum mechanical density operator onto a set of equivalent stochastic differential equations. One of the stochastic variables is termed the "weight", and its magnitude is related to the importance of the stochastic trajectory. We investigate the use of Monte Carlo algorithms to improve the sampling of the weighted trajectories and thus reduce sampling error in a simulation of quantum dynamics. The method can be applied to calculations in real time, as well as imaginary time for which Monte Carlo algorithms are more-commonly used. The method is applicable when the weight is guaranteed to be real, and we demonstrate how to ensure this is the case. Examples are given for the anharmonic oscillator, where large improvements over stochastic sampling are observed.

Mark R. Dowling; Matthew J. Davis; Peter D. Drummond; Joel F. Corney

2005-07-01

104

Monte carlo sampling approach to testing nonnested hypothesis: monte carlo results  

Microsoft Academic Search

Alternative ways of using Monte Carlo methods to implement a Cox-type test for separate families of hypotheses are considered. Monte Carlo experiments are designed to compare the finite sample performances of Pesaran and Pesaran's test, a RESET test, and two Monte Carlo hypothesis test procedures. One of the Monte Carlo tests is based on the distribution of the log-likelihood ratio

N. Coulibaly; B. Wade Brorsen

1999-01-01

105

Towards Monte Carlo Simulations on Large Nuclei August 2014 Towards Monte Carlo Simulations on Large Nuclei  

E-print Network

Towards Monte Carlo Simulations on Large Nuclei · August 2014 Towards Monte Carlo Simulations published method to compute properties on neutron matter using variational Monte Carlo simulations published a method of performing variational Monte Carlo calculations on neutron matter comprised of up

Washington at Seattle, University of - Department of Physics, Electroweak Interaction Research Group

106

Population Monte Carlo Methods/OFPR/CREST/May 5, 2003 1 Population Monte Carlo Methods  

E-print Network

Population Monte Carlo Methods/OFPR/CREST/May 5, 2003 1 Population Monte Carlo Methods Christian P. Robert Universit´e Paris Dauphine #12;Population Monte Carlo Methods/OFPR/CREST/May 5, 2003 2 1 A Benchmark example #12;Population Monte Carlo Methods/OFPR/CREST/May 5, 2003 3 Even simple models may lead

Robert, Christian P.

107

Monte Carlo Experiments: Design and Implementation.  

ERIC Educational Resources Information Center

Illustrates the design and planning of Monte Carlo simulations, presenting nine steps in planning and performing a Monte Carlo analysis from developing a theoretically derived question of interest through summarizing the results. Uses a Monte Carlo simulation to illustrate many of the relevant points. (SLD)

Paxton, Pamela; Curran, Patrick J.; Bollen, Kenneth A.; Kirby, Jim; Chen, Feinian

2001-01-01

108

Quantum Monte Carlo on Graphical Processing Units  

E-print Network

Quantum Monte Carlo on Graphical Processing Units Amos Anderson William A. Goddard III Materials Institute of Technology, Pasadena, California 91125 Abstract Quantum Monte Carlo (QMC) is among the most algorithm can match CPU single precision. Key words: Graphical Processing Units, Quantum Monte Carlo, matrix

Desbrun, Mathieu

109

Generalized Darting Monte Carlo Cristian Sminchisescu  

E-print Network

mode structure through local MCMC moves (e.g. diffusion or Hybrid Monte Carlo) but in addition alsoGeneralized Darting Monte Carlo Cristian Sminchisescu Toyota Technological Institute Chicago, USA 92697-3425, USA welling@ics.uci.edu Abstract One of the main shortcomings of Markov chain Monte Carlo

Welling, Max

110

Machine Learning ! ! ! ! ! Srihari Markov Chain Monte Carlo  

E-print Network

Machine Learning ! ! ! ! ! Srihari 1 Markov Chain Monte Carlo Sampling Methods Sargur Srihari srihari@cedar.buffalo.edu #12;Machine Learning ! ! ! ! ! Srihari 2 Topics 1. Markov Chain Monte Carlo 2 Sampling #12;Machine Learning ! ! ! ! ! Srihari 3 1. Markov Chain Monte Carlo (MCMC) · Rejection sampling

111

MONTE CARLO SOLUTION OF SCATTERING EQUATIONS  

E-print Network

of transfer (i.e. the rendering equation). We apply Monte Carlo techniques to solve this scattering equationMONTE CARLO SOLUTION OF SCATTERING EQUATIONS FOR COMPUTER GRAPHICS A DISSERTATION SUBMITTED efficiently; to our knowledge, this is the first application of Monte Carlo to solving it in any field. We

Stanford University

112

Monte Carlo Complexity of Parametric Integration  

E-print Network

Monte Carlo Complexity of Parametric Integration stefan heinrich and eug`ene sindambiwe Department The Monte Carlo complexity of computing integrals depending on a parameter is analyzed for smooth integrands that of previously developed Monte Carlo algorithms for parametric integration. 1 introduction Multivariate

Heinrich, Stefan

113

Monte Carlo Modeling of JLab Spectrometers  

Microsoft Academic Search

Monte Carlo simulations are used in both the design of spectrometers to estimate the expected performance and in the physics data taking to compare experimental results with Monte Carlo simulated results. The Monte Carlo simulations realistically take multiple scattering and energy loss effects into account in the target region, propagate the particles throught the various magnetic elements, track them through

M. Boswell; R. Ent; D. Meekins; D. Mack; P. Ulmer; H. Bitau

2000-01-01

114

Monte Carlo Modeling of JLab Spectrometers  

Microsoft Academic Search

Monte Carlo simulations are used in both the design of spectrometers to estimate the expected performance and in the physics data taking to compare experimental results with Monte Carlo simulated results. The Monte Carlo simulations realistically take multiple scattering and energy loss effects into account in the target region, propagate the particles through the various magnetic elements, track them through

Melissa Boswell

2001-01-01

115

On Monte Carlo methods for Bayesian inference  

Microsoft Academic Search

Bayesian methods are experiencing increased use for probabilistic ecological modelling. Most Bayesian inference requires the numerical approximation of analytically intractable integrals. Two methods based on Monte Carlo simulation have appeared in the ecological\\/environmental modelling literature. Though they sound similar, the Bayesian Monte Carlo (BMC) and Markov Chain Monte Carlo (MCMC) methods are very different in their efficiency and effectiveness in

Song S. Qian; Craig A. Stow; Mark E. Borsuky

2003-01-01

116

Monte Carlo Methods in Statistics Christian Robert  

E-print Network

Monte Carlo Methods in Statistics Christian Robert Universit´e Paris Dauphine and CREST, INSEE September 2, 2009 Monte Carlo methods are now an essential part of the statistician's toolbox, to the point! We recall in this note some of the advances made in the design of Monte Carlo techniques towards

Boyer, Edmond

117

MONTE CARLO METHOD AND SENSITIVITY ESTIMATIONS  

E-print Network

MONTE CARLO METHOD AND SENSITIVITY ESTIMATIONS A. de Lataillade a;#3; , S. Blanco b , Y. Clergent b on a formal basis and simple radiative transfer examples are used for illustration. Key words: Monte Carlo submitted to Elsevier Science 18 February 2002 #12; 1 Introduction Monte Carlo methods are commonly used

Dufresne, Jean-Louis

118

A Monte Carlo Study of Titrating Polyelectrolytes  

E-print Network

A Monte Carlo Study of Titrating Polyelectrolytes Magnus Ullner y and Bo J¨onsson z Physical, Sweden Journal of Chemical Physics 104, 3048­3057 (1996) Monte Carlo simulations have been used to study of the polymer more difficult and biases the conformations towards more extended structures. In the Monte Carlo

Peterson, Carsten

119

Monte Carlo Integration Lecture 2 The Problem  

E-print Network

Monte Carlo Integration Lecture 2 The Problem Let be a probability measure over the Borel -field X S and h(x) = 0 otherwise. #12;Monte Carlo Integration Lecture 2 When the problem appears to be intractable, Press et al (1992) and reference therein). For high dimensional problems, Monte Carlo methods have

Liang, Faming

120

Monte Carlo Simulations of Model Nonionic Surfactants  

E-print Network

Monte Carlo Simulations of Model Nonionic Surfactants A.P. Chatterjee and A.Z. Panagiotopoulos was studied by histogram reweight- ing grand canonical Monte Carlo simulations. Two di erent sets of site volume fractions using lattice Monte Carlo simulations performed in the canonical constant NV T ensemble

121

A Monte Carlo Study of Titrating Polyelectrolytes  

E-print Network

A Monte Carlo Study of Titrating Polyelectrolytes Magnus Ullnery and Bo Jonssonz Physical Chemistry Journal of Chemical Physics 104, 3048-3057 (1996) Monte Carlo simulations have been used to study three di the conformations towards more extended structures. In the Monte Carlo simulations presented here, focus

Peterson, Carsten

122

Thermodynamic Scaling Gibbs Ensemble Monte Carlo  

E-print Network

Thermodynamic Scaling Gibbs Ensemble Monte Carlo: A new method for determination of phase for correspondence. E­mail:azp2@cornell.edu #12; We combine Valleau's thermodynamic scaling Monte Carlo concept Monte Carlo simulations. There has been significant recent progress in molecular simulation method

123

The monte carlo newton-raphson algorithm  

Microsoft Academic Search

It is shown that the Monte Carlo Newton-Raphson algorithm is a viable alternative to the Monte Carlo EM algorithm for finding maximum likelihood estimates based on incomplete data. Both Monte Carlo procedures require simulations from the conditional distribution of the missing data given the observed data with the aid of methods like Gibbs sampling and rejective sampling. The Newton-Raphson algorithm

Anthony Y. C. Kuk; Yuk W. Cheng

1997-01-01

124

Monte Carlo Simulation for Perusal and Practice.  

ERIC Educational Resources Information Center

The meaningful investigation of many problems in statistics can be solved through Monte Carlo methods. Monte Carlo studies can help solve problems that are mathematically intractable through the analysis of random samples from populations whose characteristics are known to the researcher. Using Monte Carlo simulation, the values of a statistic are…

Brooks, Gordon P.; Barcikowski, Robert S.; Robey, Randall R.

125

Estimating reservoir parameters from seismic and electromagnetic data using stochastic rock-physics models and Markov chain Monte Carlo methods  

E-print Network

Estimating reservoir parameters from seismic and electromagnetic data using stochastic rock, and pore pressure in reservoirs using seismic and electromagnetic (EM) data. Within the Bayesian framework, unknown reservoir parameters at each pixel in space are considered as random variables and the co

Chen, Jinsong

126

Comparative Monte Carlo Efficiency by Monte Carlo Analysis  

Microsoft Academic Search

We propose a modified power method for computing the subdominant eigenvalue\\u000a$\\\\lambda_2$ of a matrix or continuous operator. Here we focus on defining\\u000asimple Monte Carlo methods for its application. The methods presented use\\u000arandom walkers of mixed signs to represent the subdominant eigenfuction.\\u000aAccordingly, the methods must cancel these signs properly in order to sample\\u000athis eigenfunction faithfully. We

B. M. Rubenstein; J. E. Gubernatis; J. D. Doll

2010-01-01

127

Extended state-space Monte Carlo methods.  

PubMed

In this paper various extensions of the parallel-tempering algorithm are developed and their properties are analyzed. The algorithms are designed to alleviate quasiergodic sampling in systems which have rough energy landscapes by coupling individual Monte Carlo chains to form a composite chain. As with parallel tempering, the procedures are based upon extending the state space to include parameters to encourage sampling mobility. One of the drawbacks of the parallel-tempering method is the stochastic nature of the Monte Carlo dynamics in the auxiliary variables which extend the state space. In this work, the possibility of improving the sampling rate by designing deterministic methods of moving through the parameter space is investigated. The methods developed in this article, which are based upon a statistical quenching and heating procedure similar in spirit to simulated annealing, are tested on a simple two-dimensional spin system (xy model) and on a model in vacuo polypeptide system. In the coupled Monte Carlo chain algorithms, we find that the net mobility of the composite chain is determined by the competition between the characteristic time of coupling between adjacent chains and the degree of overlap of their distributions. Extensive studies of all methods are carried out to obtain optimal sampling conditions. In particular, the most efficient parallel-tempering procedure is to attempt to swap configurations after very few Monte Carlo updates of the composite chains. Furthermore, it is demonstrated that, contrary to expectations, the deterministic procedure does not improve the sampling rate over that of parallel tempering. PMID:11415039

Opps, S B; Schofield, J

2001-05-01

128

Ray Microcanonical Monte Carlo Model  

NSDL National Science Digital Library

The Ray Microcanonical Monte Carlo Model implements microcanonical simulations, with the Lennard-Jones potential, by integrating the kinetic contributions to the Hamiltonian and sampling the configuration space. This model is based on a method developed by John R. Ray. The input fields are editable allowing the study of different regions of the phase diagram. Radial distribution functions are displayed and average thermodynamic properties and their fluctuations are displayed. The Ray Microcanonical Monte Carlo Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.

2012-12-10

129

Motivation Monte Carlo Methoden Quasi-Monte Carlo Methoden Folgen kleiner Diskrepanz Simulationstechniken in Finanz-und  

E-print Network

Motivation Monte Carlo Methoden Quasi-Monte Carlo Methoden Folgen kleiner Diskrepanz Simulationstechniken in Finanz- und Versicherungsmathematik #12;Motivation Monte Carlo Methoden Quasi-Monte Carlo Methoden Folgen kleiner Diskrepanz 1 Motivation 2 Monte Carlo Methoden 3 Quasi-Monte Carlo Methoden 4

Hofer, Markus

130

Bacteria Allocation Using Monte Carlo  

NSDL National Science Digital Library

This applet, created by David Hill and Lila Roberts, uses the Monte Carlo technique to simulate a count of bacteria that are present as a result of a certain sampling process. This simulation could be modified to perform other experiments. This experiment is geared towards high school calculus students or probability courses for mathematics majors in college. Students must possess a basic understanding of probability concepts before performing this experiment. Overall, it is a nice activity for a mathematics classroom.

Hill, David R.

131

Monte Carlo Methods Geoff Gordon  

E-print Network

Monte Carlo Methods Geoff Gordon ggordon@cs.cmu.edu February 9, 2006 #12;Numerical integration(-T(x)) As , have ExP (x) x Simulated annealing: track E(x) = xP(x)dx as #12;Used for: Bayes net inference Undirected Bayes net on x = x1, x2, . . .: P(x) = 1 Z j j(x) Typical inference problem: compute E(xi) Belief

Guestrin, Carlos

132

Quasi-Monte Carlo Integration  

Microsoft Academic Search

The standard Monte Carlo approach to evaluating multidimensional integrals using (pseudo)-random integration nodes is frequently used when quadrature methods are too difficult or expensive to implement. As an alternative to the random methods, it has been suggested that lower error and improved convergence may be obtained by replacing the pseudo-random sequences with more uniformly distributed sequences known as quasi-random. In

William J. Morokoff; Russel E. Caflisch

1995-01-01

133

Grand Canonical Monte Carlo Model  

NSDL National Science Digital Library

The Grand Canonical Monte Carlo Model illustrates grand canonical ensemble (µVT) Monte Carlo simulations: the chemical potential, volume and temperature are the system constraints. This means that the system has porous and diabatic walls, exchanging molecules and heat with a reservoir at constant chemical potential and temperature. The molecules interact through the Lennard-Jones. potential and fluid states at densities 0.0025 ? ? ? 0.85 and temperatures T ? 0.70 can be simulated. Although the volume is kept constant, the number of molecules fluctuates and so does the density. The aim is to reach a chemical potential approaching the imposed one. The input fields can be edited to probe different regions of the phase diagram. Chemical potentials, activity coefficients, Helmholtz free energies, entropies and their excess contributions are worked out. The Grand Canonical Monte Carlo Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.

2014-05-21

134

Some Continuous Monte Carlo Methods for the Dirichlet Problem  

Microsoft Academic Search

Monte Carlo techniques are introduced, using stochastic models which are Markov processes. This material includes the $N$-dimensional Spherical, General Spherical, and General Dirichlet Domain processes. These processes are proved to converge with probability 1, and thus to yield direct statistical estimates of the solution to the $N$-dimensional Dirichlet problem. The results are obtained without requiring any further restrictions on the

Mervin E. Muller

1956-01-01

135

Weighted Importance Sampling Techniques for Monte Carlo Radiosity  

Microsoft Academic Search

This paper presents weighted importance sampling techniques for Monte Carlo form factor computation and for stochastic Jacobi radiosity sys- tem solution. Weighted importance sampling is a generalisation of importance sampling. The basic idea is to compute a-posteriori a correc tion factor to the importance sampling estimates, based on sample weights accumulated during sampling. With proper weights, the correction factor will

Philippe Bekaert; Mateu Sbert; Yves D. Willems

2000-01-01

136

Monte Carlo Simulation of the Formation of Snowflakes  

Microsoft Academic Search

A stochastic microphysical model of snow aggregation that combines a simple aggregation model with a Monte Carlo method was developed. Explicit treatment of the shape of individual snowflakes in the new model facilitates examination of the structure of snowflakes and the relationships between the parameters of the generated snowflakes, such as mass versus diameter, in addition to comparisons with observations.

Ken-Ichi Maruyama; Yasushi Fujiyoshi

2005-01-01

137

Monte-Carlo Planning: Basic Principles and Recent Progress  

E-print Network

-horizon discounted reward, discount factor 0 total reward Bandit) UCT Monte-Carlo Tree Search #12;3 State + Reward Actions (possibly stochastic) ???? World a in state s First-order Markov model Bounded reward distribution PR(r | s, a) Probability of receiving

138

Fermion Monte Carlo without fixed nodes: A Game of Life, death and annihilation in Slater Determinant space  

E-print Network

Fermion Monte Carlo without fixed nodes: A Game of Life, death and annihilation in Slater Monte Carlo method for the simulation of correlated many- electron systems in Full Configuration of many- electron systems via stochastic methods such as Diffusion quantum Monte Carlo (DMC) [1

Alavi, Ali

139

A Chance at Monte Carlo  

NSDL National Science Digital Library

At its core, the LEGO® MINDSTORMS® NXT product provides a programmable microprocessor. Students use the NXT processor to simulate an experiment involving thousands of uniformly random points placed within a unit square. Using the underlying geometry of the experimental model, as well as the geometric definition of the constant ? (pi), students form an empirical ratio of areas to estimate a numerical value of ?. Although typically used for numerical integration of irregular shapes, in this activity, students use a Monte Carlo simulation to estimate a common but rather complex analytical form—the numerical value of the most famous irrational number, ?.

2014-09-18

140

The PHOBOS Glauber Monte Carlo  

E-print Network

``Glauber'' models are used to calculate geometric quantities in the initial state of heavy ion collisions, such as impact parameter, number of participating nucleons and initial eccentricity. The four RHIC experiments have different methods for Glauber Model calculations, leading to similar results for various geometric observables. In this document, we describe an implementation of the Monte Carlo based Glauber Model calculation used by the PHOBOS experiment. The assumptions that go in the calculation are described. A user's guide is provided for running various calculations.

B. Alver; M. Baker; C. Loizides; P. Steinberg

2008-05-28

141

Using Quasi–Monte Carlo in Practice  

Microsoft Academic Search

In the preceding chapter, we presented several constructions that can be used for quasi–Monte Carlo sampling and discussed\\u000a how to assess their quality. In this chapter, we focus on issues that arise when applying quasi–Monte Carlo methods in practice.\\u000a We first discuss randomized quasi–Monte Carlo, which, as we mentioned at the end of the previous chapter, is an essential\\u000a tool

Christiane Lemieux

142

Grid-Based Monte Carlo Application  

Microsoft Academic Search

Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the grid using the dynamic bag-of-work model. We improve the efficiency of the subtask-scheduling scheme by using an N-out-of-M strategy, and develop a Monte Carlo-specific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo grid computing. Also, we

Yaohang Li; Michael Mascagni

2002-01-01

143

Monte Carlo methods for TMD analyses  

NASA Astrophysics Data System (ADS)

Monte Carlo simulations are an indispensable tool in experimental high-energy physics. Indeed, many discoveries rely on realistic modeling of background processes. In the field of transverse-momentum-dependent parton distribution and fragmentation functions there is a clear lack of a reliable Monte Carlo physics generator that can be used in experimental and phenomenological analyses. The need for such Monte Carlo generators, the status of some solutions and prospects are discussed.

Schnell, Gunar

2015-01-01

144

Monte Carlo One-dimension Integration Model  

NSDL National Science Digital Library

The Monte Carlo One-dimension Integration Model illustrates the Monte Carlo integration algorithm to compute the integral of a function f(x). The simulation allows you to select the number of random points, to make an automatic fit to the function graph in the Y axis (thus improving the accuracy of the estimation), and to display the points or not. The simulation computes the actual value of the integral using a Romberg algorithm to test the Monte Carlo integral approximation.

Franciscouembre

2012-02-08

145

Hybrid S[sub N]\\/Monte Carlo research and results  

Microsoft Academic Search

The neutral particle transport equation is solved by a hybrid method that iteratively couples regions where deterministic (S[sub N]) and stochastic (Monte Carlo) methods are applied. The Monte Carlo and S[sub N] regions are fully coupled in the sense that no assumption is made about geometrical separation or decoupling. The hybrid Monte Carlo\\/S[sub N] method provides a new means of

1993-01-01

146

Hybrid S{sub N}\\/Monte Carlo research and results  

Microsoft Academic Search

The neutral particle transport equation is solved by a hybrid method that iteratively couples regions where deterministic (S{sub N}) and stochastic (Monte Carlo) methods are applied. The Monte Carlo and S{sub N} regions are fully coupled in the sense that no assumption is made about geometrical separation or decoupling. The hybrid Monte Carlo\\/S{sub N} method provides a new means of

1993-01-01

147

Chemical application of diffusion quantum Monte Carlo  

NASA Technical Reports Server (NTRS)

The diffusion quantum Monte Carlo (QMC) method gives a stochastic solution to the Schroedinger equation. This approach is receiving increasing attention in chemical applications as a result of its high accuracy. However, reducing statistical uncertainty remains a priority because chemical effects are often obtained as small differences of large numbers. As an example, the single-triplet splitting of the energy of the methylene molecule CH sub 2 is given. The QMC algorithm was implemented on the CYBER 205, first as a direct transcription of the algorithm running on the VAX 11/780, and second by explicitly writing vector code for all loops longer than a crossover length C. The speed of the codes relative to one another as a function of C, and relative to the VAX, are discussed. The computational time dependence obtained versus the number of basis functions is discussed and this is compared with that obtained from traditional quantum chemistry codes and that obtained from traditional computer architectures.

Reynolds, P. J.; Lester, W. A., Jr.

1984-01-01

148

Monte Carlo surface flux tallies  

SciTech Connect

Particle fluxes on surfaces are difficult to calculate with Monte Carlo codes because the score requires a division by the surface-crossing angle cosine, and grazing angles lead to inaccuracies. We revisit the standard practice of dividing by half of a cosine 'cutoff' for particles whose surface-crossing cosines are below the cutoff. The theory behind this approximation is sound, but the application of the theory to all possible situations does not account for two implicit assumptions: (1) the grazing band must be symmetric about 0, and (2) a single linear expansion for the angular flux must be applied in the entire grazing band. These assumptions are violated in common circumstances; for example, for separate in-going and out-going flux tallies on internal surfaces, and for out-going flux tallies on external surfaces. In some situations, dividing by two-thirds of the cosine cutoff is more appropriate. If users were able to control both the cosine cutoff and the substitute value, they could use these parameters to make accurate surface flux tallies. The procedure is demonstrated in a test problem in which Monte Carlo surface fluxes in cosine bins are converted to angular fluxes and compared with the results of a discrete ordinates calculation.

Favorite, Jeffrey A [Los Alamos National Laboratory

2010-11-19

149

Monte Carlo Form-Finding Method for Tensegrity Structures  

NASA Astrophysics Data System (ADS)

In this paper, we propose a Monte Carlo-based approach to solve tensegrity form-finding problems. It uses a stochastic procedure to find the deterministic equilibrium configuration of a tensegrity structure. The suggested Monte Carlo form-finding (MCFF) method is highly efficient because it does not involve complicated matrix operations and symmetry analysis and it works for arbitrary initial configurations. Both regular and non-regular tensegrity problems of large scale can be solved. Some representative examples are presented to demonstrate the efficiency and accuracy of this versatile method.

Li, Yue; Feng, Xi-Qiao; Cao, Yan-Ping

2010-05-01

150

Quasi-Monte Carlo and Monte Carlo Methods and their Application in Finance  

Microsoft Academic Search

We give an introduction to and a survey on the use of Quasi-Monte Carlo and of Monte Carlo methods especially in option pricing and in risk man- agement. We concentrate on new techniques from the Quasi-Monte Carlo theory.

G. Larcherand; G. Leobacher

151

Monte Carlo Method for Calculating the Electrostatic Energy of a Molecule  

E-print Network

Monte Carlo Method for Calculating the Electrostatic Energy of a Molecule Michael Mascagni1 ,2, coupled by boundary conditions. A Monte Carlo estimate for the potential point values, their derivatives to a stochastic differential equation via a first-order Euler scheme (see e.g. [11]). This approach was applied [4

Mascagni, Michael

152

Coupling Deterministic and Monte Carlo Transport Methods for the Simulation of Gamma-Ray Spectroscopy Scenarios  

Microsoft Academic Search

Simulation is often used to predict the response of gamma-ray spectrometers in technology viability and comparative studies for homeland and national security scenarios. Candidate radiation transport methods generally fall into one of two broad categories: stochastic (Monte Carlo) and deterministic. Monte Carlo methods are the most heavily used in the detection community and are particularly effective for calculating pulse-height spectra

Leon E. Smith; Christopher J. Gesh; Richard T. Pagh; Erin A. Miller; Mark W. Shaver; Eric D. Ashbaker; Michael T. Batdorf; J. Edward Ellis; William R. Kaye; Ronald J. McConn; George H. Meriwether; Jennifer J. Ressler; Andrei B. Valsan; Todd A. Wareing

2008-01-01

153

Fission Matrix Capability for MCNP Monte Carlo  

SciTech Connect

In a Monte Carlo criticality calculation, before the tallying of quantities can begin, a converged fission source (the fundamental eigenvector of the fission kernel) is required. Tallies of interest may include powers, absorption rates, leakage rates, or the multiplication factor (the fundamental eigenvalue of the fission kernel, k{sub eff}). Just as in the power iteration method of linear algebra, if the dominance ratio (the ratio of the first and zeroth eigenvalues) is high, many iterations of neutron history simulations are required to isolate the fundamental mode of the problem. Optically large systems have large dominance ratios, and systems containing poor neutron communication between regions are also slow to converge. The fission matrix method, implemented into MCNP[1], addresses these problems. When Monte Carlo random walk from a source is executed, the fission kernel is stochastically applied to the source. Random numbers are used for: distances to collision, reaction types, scattering physics, fission reactions, etc. This method is used because the fission kernel is a complex, 7-dimensional operator that is not explicitly known. Deterministic methods use approximations/discretization in energy, space, and direction to the kernel. Consequently, they are faster. Monte Carlo directly simulates the physics, which necessitates the use of random sampling. Because of this statistical noise, common convergence acceleration methods used in deterministic methods do not work. In the fission matrix method, we are using the random walk information not only to build the next-iteration fission source, but also a spatially-averaged fission kernel. Just like in deterministic methods, this involves approximation and discretization. The approximation is the tallying of the spatially-discretized fission kernel with an incorrect fission source. We address this by making the spatial mesh fine enough that this error is negligible. As a consequence of discretization we get a spatially low-order kernel, the fundamental eigenvector of which should converge faster than that of continuous kernel. We can then redistribute the fission bank to match the fundamental fission matrix eigenvector, effectively eliminating all higher modes. For all computations here biasing is not used, with the intention of comparing the unaltered, conventional Monte Carlo process with the fission matrix results. The source convergence of standard Monte Carlo criticality calculations are, to some extent, always subject to the characteristics of the problem. This method seeks to partially eliminate this problem-dependence by directly calculating the spatial coupling. The primary cost of this, which has prevented widespread use since its inception [2,3,4], is the extra storage required. To account for the coupling of all N spatial regions to every other region requires storing N{sup 2} values. For realistic problems, where a fine resolution is required for the suppression of discretization error, the storage becomes inordinate. Two factors lead to a renewed interest here: the larger memory available on modern computers and the development of a better storage scheme based on physical intuition. When the distance between source and fission events is short compared with the size of the entire system, saving memory by accounting for only local coupling introduces little extra error. We can gain other information from directly tallying the fission kernel: higher eigenmodes and eigenvalues. Conventional Monte Carlo cannot calculate this data - here we have a way to get new information for multiplying systems. In Ref. [5], higher mode eigenfunctions are analyzed for a three-region 1-dimensional problem and 2-dimensional homogenous problem. We analyze higher modes for more realistic problems. There is also the question of practical use of this information; here we examine a way of using eigenmode information to address the negative confidence interval bias due to inter-cycle correlation. We apply this method mainly to four problems: 2D pressurized water reactor (PWR) [6],

Carney, Sean E. [Los Alamos National Laboratory; Brown, Forrest B. [Los Alamos National Laboratory; Kiedrowski, Brian C. [Los Alamos National Laboratory; Martin, William R. [Los Alamos National Laboratory

2012-09-05

154

MCM for PDEs Monte Carlo Methods for  

E-print Network

MCM for PDEs Monte Carlo Methods for Partial Differential Equations Prof. Michael Mascagni University, Tallahassee, FL 32306 USA E-mail: mascagni@fsu.edu or mascagni@math.ethz.ch URL: http-Diffusion Equations Monte Carlo Methods for PDEs from Fluid Mechanics Probabilistic Representations for Other PDEs

Mascagni, Michael

155

Metropolis Monte Carlo Simulation: Q & A  

E-print Network

Metropolis Monte Carlo Simulation: Q & A Aiichiro Nakano Collaboratory for Advanced Computing.7 1 0 0.4286 0.5714 #12;A Metropolis Monte Carlo = 3/7 4 /7 Your only · Kinetic MC: Given transition-rate matrix (calculated e.g. based on the transition state theory) & initial

Southern California, University of

156

Applicability and Robustness of Monte Carlo Algorithms  

E-print Network

Applicability and Robustness of Monte Carlo Algorithms for Very Large Linear Algebra Problems Ivan-21 June #12;Outline · Motivation · Markov Chain Monte Carlo ­ Bilinear Forms of Matrix Powers O(n3 ) sequential steps (e.g. Gaussian elimination, Gauss-Jordan methods, LU-factorisation) is used

Dimov, Ivan

157

A NOTE ON MONTE CARLO PRIMALITY  

E-print Network

A NOTE ON MONTE CARLO PRIMALITY TESTS AND ALGORITHMIC INFORMATION THEORY Communications on Pure information content, i.e., has maximal algorithmic randomness, then one obtains an error-free test-delimiting." #12;A Note on Monte Carlo Primality Tests 3 may have their members printed in arbitrary order. X can

Goodman, James R.

158

Optimal Monte Carlo Algorithms Ivan T. Dimov  

E-print Network

Optimal Monte Carlo Algorithms Ivan T. Dimov Institute for Parallel Processing Department Centre University of Reading Whiteknights, PO Box 217, Reading, RG6 6AH, UK E-mail: I.T.Dimov@reading.ac.uk; ivdimov@bas.bg Web site: http://www.personal.rdg.ac.uk/ sis04itd/ Abstract The question "what Monte Carlo

Dimov, Ivan

159

Physically Based Rendering Monte Carlo Integration  

E-print Network

Physically Based Rendering (600.657) Monte Carlo Integration #12;Goals In performing sampling we sampling #12;Russian Roulette Given a PDF p and a function f, the Monte- Carlo estimate of the integral is probability q(x). 2. Choose replacement value c (e.g. c=0). 3. With probability q(x) do not evaluate

Kazhdan, Michael

160

Current advances in Monte Carlo methods  

Microsoft Academic Search

In this review paper, we outline the principles of Monte Carlo simulation of fluid mixtures, with emphasis on methods for calculation of free energies and phase equilibria. We begin with a brief introduction to common intermolecular potential models. We discuss density-dependent potentials that can accurately represent properties over a wide range of densities. A number of Monte Carlo techniques are

Athanassios Z. Panagiotopoulos

1996-01-01

161

Quantum Monte Carlo Endstation for Petascale; Computing  

Microsoft Academic Search

NCSU research group has been focused on accomplising the key goals of this initiative: establishing new generation of quantum Monte Carlo (QMC) computational tools as a part of Endstation petaflop initiative for use at the DOE ORNL computational facilities and for use by computational electronic structure community at large; carrying out high accuracy quantum Monte Carlo demonstration projects in application

Lubos Mitas

2011-01-01

162

Power of the Sequential Monte Carlo Test  

Microsoft Academic Search

Many statistical tests obtain their p-value from a Monte Carlo sample of m values of the test statistic under the null hypothesis. The number m of simulations is fixed by the researcher prior to any analysis. In contrast, the sequential Monte Carlo test does not fix the number of simulations in advance. It keeps simulating the test statistics until it

I. Silva; R. Assunção; M. Costa

2009-01-01

163

Bayesian optimization using sequential Monte Carlo  

E-print Network

Bayesian optimization using sequential Monte Carlo Romain Benassi, Julien Bect, and Emmanuel a Sequential Monte Carlo (SMC) approach. 1 Overview of the contribution proposed We consider the problem the case in design and analysis of computer experiments [2]. However, going from the general framework

Paris-Sud XI, Université de

164

Uncertainty analysis in Monte Carlo criticality computations  

Microsoft Academic Search

Uncertainty analysis is imperative for nuclear criticality risk assessments when using Monte Carlo neutron transport methods to predict the effective neutron multiplication factor (keff) for fissionable material systems. For the validation of Monte Carlo codes for criticality computations against benchmark experiments, code accuracy and precision are measured by both the computational bias and uncertainty in the bias. The uncertainty in

Qi Ao

165

What Monte Carlo methods cannot do  

Microsoft Academic Search

Although extremely flexible and obviously useful for many risk assessment problems, Monte Carlo methods have four significant limitations that risk analysts should keep in mind. (1) Like most methods based on probability theory, Monte Carlo methods are data?intensive. Consequently, they usually cannot produce results unless a considerable body of empirical information has been collected, or unless the analyst is willing

Scott Ferson

1996-01-01

166

Monte Carlo Methods in Geophysical Inverse Problems  

Microsoft Academic Search

Monte Carlo inversion techniques were first used by Earthscientists more than 30 years ago. Since that time they havebeen applied to a wide range of problems, from the inversion offree oscillation data for whole Earth seismic structure tostudies at the meter-scale lengths encountered in explorationseismology. This paper traces the development and application ofMonte Carlo methods for inverse problems in the

Malcolm Sambridge; Klaus Mosegaard

2002-01-01

167

Monte Carlo methods for security pricing  

Microsoft Academic Search

The Monte Carlo approach has proved to be a valuable and flexible computational tool in modern finance. This paper discusses some of the recent applications of the Monte Carlo method to security pricing problems, with emphasis on improvements in efficiency. We first review some variance reduction methods that have proved useful in finance. Then we describe the use of deterministic

Phelim Boyle; Mark Broadie; Paul Glasserman

1997-01-01

168

Smart Darting Monte Carlo Ioan Andricioaeia)  

E-print Network

Smart Darting Monte Carlo Ioan Andricioaeia) and John E. Straubb) Department of Chemistry, Boston, Los Alamos, New Mexico 87545 Received 18 September 2000; accepted 2 February 2001 The ``Smart Walking of the algorithm, Smart Darting Monte Carlo, which obeys the detailed balance condition, is proposed. Calculations

Straub, John E.

169

Monte Carlo Application ToolKit (MCATK)  

NASA Astrophysics Data System (ADS)

The Monte Carlo Application ToolKit (MCATK) is a component-based software library designed to build specialized applications and to provide new functionality for existing general purpose Monte Carlo radiation transport codes. We will describe MCATK and its capabilities along with presenting some verification and validations results.

Adams, Terry; Nolen, Steve; Sweezy, Jeremy; Zukaitis, Anthony; Campbell, Joann; Goorley, Tim; Greene, Simon; Aulwes, Rob

2014-06-01

170

MONTE CARLO METHODS IN GEOPHYSICAL INVERSE Malcolm Sambridge  

E-print Network

MONTE CARLO METHODS IN GEOPHYSICAL INVERSE PROBLEMS Malcolm Sambridge Research School Earth 27 2000; revised 15 December 2001; accepted 9 September published 5 December Monte Carlo inversion encountered in exploration seismology. traces development application Monte Carlo methods inverse problems

Sambridge, Malcolm

171

Optimised Iteration in Coupled Monte Carlo - Thermal-Hydraulics Calculations  

NASA Astrophysics Data System (ADS)

This paper describes an optimised iteration scheme for the number of neutron histories and the relaxation factor in successive iterations of coupled Monte Carlo and thermal-hydraulic reactor calculations based on the stochastic iteration method. The scheme results in an increasing number of neutron histories for the Monte Carlo calculation in successive iteration steps and a decreasing relaxation factor for the spatial power distribution to be used as input to the thermal-hydraulics calculation. The theoretical basis is discussed in detail and practical consequences of the scheme are shown, among which a nearly linear increase per iteration of the number of cycles in the Monte Carlo calculation. The scheme is demonstrated for a full PWR type fuel assembly. Results are shown for the axial power distribution during several iteration steps. A few alternative iteration method are also tested and it is concluded that the presented iteration method is near optimal.

Hoogenboom, J. Eduard; Dufek, Jan

2014-06-01

172

Monte Carlo Simulations for Radiobiology  

NASA Astrophysics Data System (ADS)

The relationship between tumor response and radiation is currently modeled as dose, quantified on the mm or cm scale through measurement or simulation. This does not take into account modern knowledge of cancer, including tissue heterogeneities and repair mechanisms. We perform Monte Carlo simulations utilizing Geant4 to model radiation treatment on a cellular scale. Biological measurements are correlated to simulated results, primarily the energy deposit in nuclear volumes. One application is modeling dose enhancement through the use of high-Z materials, such gold nanoparticles. The model matches in vitro data and predicts dose enhancement ratios for a variety of in vivo scenarios. This model shows promise for both treatment design and furthering our understanding of radiobiology.

Ackerman, Nicole; Bazalova, Magdalena; Chang, Kevin; Graves, Edward

2012-02-01

173

Monte Carlo telescope performance modeling  

NASA Astrophysics Data System (ADS)

We describe and demonstrate a telescope performance model based on Monte Carlo simulations. As a specific example, we apply this method to our delivered image quality error budgets for the Advanced Technology Solar Telescope (ATST). The ATST site survey database provides us with probability distributions for parameters that affect image quality, like wind velocity and Fried"s seeing parameter. The histograms characterizing these parameters can be sampled many times randomly to yield fact-based predictions of system performance. From this we are able to estimate the fraction of the time that a given site will meet or exceed the performance goals of the telescope. The calculations are performed using Crystal Ball, an after-market add-in for Microsoft Excel marketed by Decisioneering, Inc. of Denver Colorado.

Hubbard, Robert P.; Oschmann, Jacobus M., Jr.

2004-09-01

174

Importance iteration in MORSE Monte Carlo calculations  

SciTech Connect

an expression to calculate point values (the expected detector response of a particle emerging from a collision or the source) is derived and implemented in the MORSE-SGC/S Monte Carlo code. It is outlined how these point values can be smoothed as a function of energy and as a function of the optical thickness between the detector and the source. The smoothed point values are subsequently used to calculate the biasing parameters of the Monte Carlo runs to follow. The method is illustrated by an example that shows that the obtained biasing parameters lead to a more efficient Monte Carlo calculation.

Kloosterman, J.L.; Hoogenboom, J.E. (Delft Univ. of Technology (Netherlands). Interfaculty Reactor Institute)

1994-05-01

175

Monte Carlo Shower Counter Studies  

NASA Technical Reports Server (NTRS)

Activities and accomplishments related to the Monte Carlo shower counter studies are summarized. A tape of the VMS version of the GEANT software was obtained and installed on the central computer at Gallaudet University. Due to difficulties encountered in updating this VMS version, a decision was made to switch to the UNIX version of the package. This version was installed and used to generate the set of data files currently accessed by various analysis programs. The GEANT software was used to write files of data for positron and proton showers. Showers were simulated for a detector consisting of 50 alternating layers of lead and scintillator. Each file consisted of 1000 events at each of the following energies: 0.1, 0.5, 2.0, 10, 44, and 200 GeV. Data analysis activities related to clustering, chi square, and likelihood analyses are summarized. Source code for the GEANT user subprograms and data analysis programs are provided along with example data plots.

Snyder, H. David

1991-01-01

176

Area Estimates by Monte Carlo Simulation  

NSDL National Science Digital Library

This demo estimates the area of a circle or triangle using a probability experiment employing the Monte Carlo technique. We also indicate how to use our approach to estimate the area of a polygonal region.

Roberts, Lila F.

2001-06-02

177

Renormalization Group by Monte Carlo Methods  

Microsoft Academic Search

I discuss the basic ideas in applying the Monte Carlo methods to the renormalization-group study of static and dynamic critical phenomena within the framework of a kinetic Ising model. Simple calculations demonstrating these ideas are presented.

Shang-Keng Ma

1976-01-01

178

Analytical Applications of Monte Carlo Techniques.  

ERIC Educational Resources Information Center

Described are analytical applications of the theory of random processes, in particular solutions obtained by using statistical procedures known as Monte Carlo techniques. Supercomputer simulations, sampling, integration, ensemble, annealing, and explicit simulation are discussed. (CW)

Guell, Oscar A.; Holcombe, James A.

1990-01-01

179

Markov Chain Monte Carlo Linkage Analysis Methods  

Microsoft Academic Search

As alluded to in the chapter “Linkage Analysis of Qualitative Traits”, neither the Elston–Steward algorithm nor the Lander–Green\\u000a approach is amenable to genetic data from large complex pedigrees and a large number of markers. In such cases, Monte Carlo\\u000a estimation methods provide a viable alternative to the exact solutions. Two types of Monte Carlo methods have been developed\\u000a for linkage

Robert P. Igo; Yuqun Luo; Shili Lin

180

Improving Monte Carlo Efficiency by Increasing Variance  

Microsoft Academic Search

This paper compares the performances of two well-known Monte Carlo procedures for estimating an unknown quantity as the size of the problem grows. One method based on the standard Monte Carlo approach generates K i.i.d. data points. The other derives its data from a single K-step sample path generated by a Markov chain. The paper gives necessary and sufficient conditions

G. S. Fishman; V. G. Kulkarni

1992-01-01

181

Bandit based Monte-Carlo Planning  

Microsoft Academic Search

For large state-space Markovian Decision Problems Monte- Carlo planning is one of the few viable approaches to flnd near-optimal solutions. In this paper we introduce a new algorithm, UCT, that ap- plies bandit ideas to guide Monte-Carlo planning. In flnite-horizon or discounted MDPs the algorithm is shown to be consistent and flnite sample bounds are derived on the estimation error

Levente Kocsis; Csaba Szepesvari

2006-01-01

182

Multiple quadrature by Monte Carlo techniques  

E-print Network

MULTIPLE QUADRATURE BY MONTE CARLO TECHNIQUES A Thesis by JOHN DIETRICH VOSS Submitted to the Graduate College of the Texas A&M University in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE May 1966 Major Subject...: Mathematics MULTIPLE QUADRATURE BY MONTE CARLO TECHNIQUES A Thesis by JOHN DIETRICH VOSS Approved as to style and content by: (Chairman of Co ittee) Ec (Head of Department) (M ember) ~ ember) May 1966 457988 ACKNOWLEDGMENTS I am deeply indebted...

Voss, John Dietrich

1966-01-01

183

Monte Carlo Methods for Dose Calculations  

NASA Astrophysics Data System (ADS)

Monte Carlo (MC) methods are increasingly being used at ion beam therapy (IBT) centers to support various dosimetric aspects of treatment planning, from characterization of the beam delivery to forward recalculation of treatment plans. This chapter will review the basic principles of Monte Carlo methods for dose calculations in therapy with protons and heavier ions, discussing the roadmap for clinical application and ongoing investigations at different IBT centers.

Parodi, Katia

184

Extra Chance Generalized Hybrid Monte Carlo  

NASA Astrophysics Data System (ADS)

We study a method, Extra Chance Generalized Hybrid Monte Carlo, to avoid rejections in the Hybrid Monte Carlo method and related algorithms. In the spirit of delayed rejection, whenever a rejection would occur, extra work is done to find a fresh proposal that, hopefully, may be accepted. We present experiments that clearly indicate that the additional work per sample carried out in the extra chance approach clearly pays in terms of the quality of the samples generated.

Campos, Cédric M.; Sanz-Serna, J. M.

2015-01-01

185

Multiple-time-stepping generalized hybrid Monte Carlo methods  

NASA Astrophysics Data System (ADS)

Performance of the generalized shadow hybrid Monte Carlo (GSHMC) method [1], which proved to be superior in sampling efficiency over its predecessors [2-4], molecular dynamics and hybrid Monte Carlo, can be further improved by combining it with multi-time-stepping (MTS) and mollification of slow forces. We demonstrate that the comparatively simple modifications of the method not only lead to better performance of GSHMC itself but also allow for beating the best performed methods, which use the similar force splitting schemes. In addition we show that the same ideas can be successfully applied to the conventional generalized hybrid Monte Carlo method (GHMC). The resulting methods, MTS-GHMC and MTS-GSHMC, provide accurate reproduction of thermodynamic and dynamical properties, exact temperature control during simulation and computational robustness and efficiency. MTS-GHMC uses a generalized momentum update to achieve weak stochastic stabilization to the molecular dynamics (MD) integrator. MTS-GSHMC adds the use of a shadow (modified) Hamiltonian to filter the MD trajectories in the HMC scheme. We introduce a new shadow Hamiltonian formulation adapted to force-splitting methods. The use of such Hamiltonians improves the acceptance rate of trajectories and has a strong impact on the sampling efficiency of the method. Both methods were implemented in the open-source MD package ProtoMol and were tested on a water and a protein systems. Results were compared to those obtained using a Langevin Molly (LM) method [5] on the same systems. The test results demonstrate the superiority of the new methods over LM in terms of stability, accuracy and sampling efficiency. This suggests that putting the MTS approach in the framework of hybrid Monte Carlo and using the natural stochasticity offered by the generalized hybrid Monte Carlo lead to improving stability of MTS and allow for achieving larger step sizes in the simulation of complex systems.

Escribano, Bruno; Akhmatskaya, Elena; Reich, Sebastian; Azpiroz, Jon M.

2015-01-01

186

An Improved Monte Carlo Algorithm for Elastic Electron Backscattering  

E-print Network

An Improved Monte Carlo Algorithm for Elastic Electron Backscattering from Surfaces Ivan T. Dimov of electrons satisfies an integral equation, which might be solved by Monte Carlo methods. The Monte Carlo ap process. We introduce different weights in the Monte Carlo algorithm, which de- crease the variance. We

Dimov, Ivan

187

Monte Carlo Go Has a Way to Go  

Microsoft Academic Search

Monte Carlo Go is a promising method to improve the perfor- mance of computer Go programs. This approach determines the next move to play based on many Monte Carlo samples. This paper examines the relative advantages of additional samples and enhancements for Monte Carlo Go. By par- allelizing Monte Carlo Go, we could increase sample sizes by two orders of

Haruhiro Yoshimoto; Kazuki Yoshizoe; Tomoyuki Kaneko; Akihiro Kishimoto; Kenjiro Taura

2006-01-01

188

Monte Carlo data association for multiple target tracking Rickard Karlsson  

E-print Network

Monte Carlo data association for multiple target tracking Rickard Karlsson Dept. of Electrical, these estimation methods may lead to non-optimal solutions. The sequential Monte Carlo methods, or particle filters chose the number of particles. 2 Sequential Monte Carlo methods Monte Carlo techniques have been

Gustafsson, Fredrik

189

A Monte Carlo method for solving unsteady adjoint equations  

E-print Network

A Monte Carlo method for solving unsteady adjoint equations Qiqi Wang a,*, David Gleich a , Amin on this technique and uses a Monte Carlo linear solver. The Monte Carlo solver yields a forward-time algorithm' equation, the Monte Carlo approach is faster for a large class of problems while preserving sufficient

Wang, Qiqi

190

Monte-Carlo vs. Bulk Conductivity Modeling of RF  

E-print Network

Monte-Carlo vs. Bulk Conductivity Modeling of RF Breakdown of Helium* Carsten Thoma, Thomas Hughes distribution function can be quite non-Maxwellian #12;2 approaches to simulating weakly- ionized plasma · Monte-Carlo with He at STP. #12;Monte Carlo Scattering Algorithm* · Implemented a Monte Carlo scattering algorithm

Kaganovich, Igor

191

Monte Carlo data association for multiple target tracking Rickard Karlsson  

E-print Network

Monte Carlo data association for multiple target tracking Rickard Karlsson Dept. of Electrical, these estimation methods may lead to non­optimal solutions. The sequential Monte Carlo methods, or particle filters chose the number of particles. 2 Sequential Monte Carlo methods Monte Carlo techniques have been

Gustafsson, Fredrik

192

The Monte Carlo Method and Software Reliability Theory  

E-print Network

1 The Monte Carlo Method and Software Reliability Theory Brian Korver1 briank@cs.pdx.edu TR 94-1. February 18, 1994 1.0 Abstract The Monte Carlo method of reliability prediction is useful when system for valid, nontrivial input data and an external oracle. 2.0 The Monte Carlo Method The Monte Carlo method

Pratt, Vaughan

193

Monte Carlo methods for fissured porous media: gridless approaches  

E-print Network

Monte Carlo methods for fissured porous media: gridless approaches Antoine Lejay1, -- Projet OMEGA (INRIA / Institut ´Elie Cartan, Nancy) Abstract: In this article, we present two Monte Carlo methods) Published in Monte Carlo Methods and Applications. Proc. of the IV IMACS Seminar on Monte Carlo Methods

Paris-Sud XI, Université de

194

Bayesian Posterior Comprehension via Message from Monte Carlo  

E-print Network

Bayesian Posterior Comprehension via Message from Monte Carlo Leigh J. Fitzgibbon, David L. Dowe Markov Chain Monte Carlo methods. The Message from Monte Carlo methodology is illustrated for binary Length, MML, MCMC, RJMCMC, Message from Monte Carlo, MMC, posterior summary, epitome, Bayesian Posterior

Allison, Lloyd

195

A Hybrid Monte Carlo Method for Accurate and Efficient  

E-print Network

A Hybrid Monte Carlo Method for Accurate and Efficient Subsurface Scattering Li, Pellacini #12;Previous work · Accurate for all materials, but inefficient ­ Monte Carlo path/light tracing · Accurate and Efficient for all materials ­ As accurate as Monte Carlo ­ More efficient than Monte Carlo

Pellacini, Fabio

196

Monte Carlo analysis of inverse problems  

Microsoft Academic Search

Monte Carlo methods have become important in analysis of nonlinear inverse problems where no analytical expression for the forward relation between data and model parameters is available, and where linearization is unsuccessful. In such cases a direct mathematical treatment is impossible, but the forward relation materializes itself as an algorithm allowing data to be calculated for any given model. Monte

Klaus Mosegaard; Malcolm Sambridge

2002-01-01

197

Monte Carlo Integration This chapter gives an introduction to Monte Carlo integration. The main goals are to review  

E-print Network

Chapter 2 Monte Carlo Integration This chapter gives an introduction to Monte Carlo integration useful in computer graphics. Good references on Monte Carlo methods include Kalos & Whitlock [1986 for Monte Carlo applications to neutron transport problems; Lewis & Miller [1984] is a good source

Stanford University

198

Ada Numerica (1998), pp. 1-49 Cambridge University Press, 1998 Monte Carlo and quasi-Monte Carlo  

E-print Network

Ada Numerica (1998), pp. 1-49 © Cambridge University Press, 1998 Monte Carlo and quasi-Monte Carlo-mail: caiflisch@math.ucla.edu Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~1 ^2 ), is independent of dimension, which shows Monte Carlo to be very robust but also

Li, Tiejun

199

Einfuhrung Die Monte-Carlo-Simulation des Pfadintegrals Anwendung Ausblick Monte-Carlo-Simulation des Pfadintegrals in der  

E-print Network

Einf¨uhrung Die Monte-Carlo-Simulation des Pfadintegrals Anwendung Ausblick Monte-Carlo Humboldt-Universit¨at zu Berlin 20. Januar 2010 Christian Wiese Monte-Carlo-Simulation des Pfadintegrals #12;Einf¨uhrung Die Monte-Carlo-Simulation des Pfadintegrals Anwendung Ausblick Inhaltsverzeichnis 1

200

Das Ising-Modell und Monte-Carlo-Simulation Das Ising-Modell und Monte-Carlo-Simulation  

E-print Network

Das Ising-Modell und Monte-Carlo-Simulation Das Ising-Modell und Monte-Carlo-Simulation Aljoscha Rheinwalt 14. Januar 2009 Betreuender Professor: Prof. M. M¨uller-Preu�ker #12;Das Ising-Modell und Monte-Carlo-Simulation Gliederung Gliederung Ising-Modell Definition Anwendungen Numerische Analyse Statistische Beschreibung Monte-Carlo

201

An Efficient Monte Carlo Method for Optimal Control Problems with Uncertainty  

Microsoft Academic Search

A general framework is proposed for what we call the sensitivity derivative Monte Carlo (SDMC) solution of optimal control problems with a stochastic parameter. This method employs the residual in the first-order Taylor series expansion of the cost functional in terms of the stochastic parameter rather than the cost functional itself. A rigorous estimate is derived for the variance of

Yanzhao Cao; M. Y. Hussaini; T. A. Zang

2003-01-01

202

Approaching chemical accuracy with quantum Monte Carlo  

NASA Astrophysics Data System (ADS)

A quantum Monte Carlo study of the atomization energies for the G2 set of molecules is presented. Basis size dependence of diffusion Monte Carlo atomization energies is studied with a single determinant Slater-Jastrow trial wavefunction formed from Hartree-Fock orbitals. With the largest basis set, the mean absolute deviation from experimental atomization energies for the G2 set is 3.0 kcal/mol. Optimizing the orbitals within variational Monte Carlo improves the agreement between diffusion Monte Carlo and experiment, reducing the mean absolute deviation to 2.1 kcal/mol. Moving beyond a single determinant Slater-Jastrow trial wavefunction, diffusion Monte Carlo with a small complete active space Slater-Jastrow trial wavefunction results in near chemical accuracy. In this case, the mean absolute deviation from experimental atomization energies is 1.2 kcal/mol. It is shown from calculations on systems containing phosphorus that the accuracy can be further improved by employing a larger active space.

Petruzielo, F. R.; Toulouse, Julien; Umrigar, C. J.

2012-03-01

203

The Monte-Carlo Revolution in Go Remi Coulom  

E-print Network

The Monte-Carlo Revolution in Go R´emi Coulom Universit´e Charles de Gaulle, INRIA, CNRS, Lille, France January, 2009 JFFoS'2008: Japanese-French Frontiers of Science Symposium #12;Introduction Monte configurations R´emi Coulom The Monte Carlo Revolution in Go 2 / 12 #12;Introduction Monte-Carlo Tree Search

Coulom, Rémi - Groupe de Recherche sur l'Apprentissage Automatique, Université Charles de Gaulle

204

Advanced interacting sequential Monte Carlo sampling for inverse scattering  

NASA Astrophysics Data System (ADS)

The following electromagnetism (EM) inverse problem is addressed. It consists in estimating the local radioelectric properties of materials recovering an object from global EM scattering measurements, at various incidences and wave frequencies. This large scale ill-posed inverse problem is explored by an intensive exploitation of an efficient 2D Maxwell solver, distributed on high performance computing machines. Applied to a large training data set, a statistical analysis reduces the problem to a simpler probabilistic metamodel, from which Bayesian inference can be performed. Considering the radioelectric properties as a hidden dynamic stochastic process that evolves according to the frequency, it is shown how advanced Markov chain Monte Carlo methods—called sequential Monte Carlo or interacting particles—can take benefit of the structure and provide local EM property estimates.

Giraud, F.; Minvielle, P.; Del Moral, P.

2013-09-01

205

Continuum variational and diffusion quantum Monte Carlo calculations.  

PubMed

This topical review describes the methodology of continuum variational and diffusion quantum Monte Carlo calculations. These stochastic methods are based on many-body wavefunctions and are capable of achieving very high accuracy. The algorithms are intrinsically parallel and well suited to implementation on petascale computers, and the computational cost scales as a polynomial in the number of particles. A guide to the systems and topics which have been investigated using these methods is given. The bulk of the article is devoted to an overview of the basic quantum Monte Carlo methods, the forms and optimization of wavefunctions, performing calculations under periodic boundary conditions, using pseudopotentials, excited-state calculations, sources of calculational inaccuracy, and calculating energy differences and forces. PMID:21386247

Needs, R J; Towler, M D; Drummond, N D; López Ríos, P

2010-01-20

206

Bayesian spectral deconvolution with the exchange Monte Carlo method.  

PubMed

An analytical method to deconvolute spectral data into a number of simple bands is extremely important in the analysis of the chemical properties of matter. However, there are two fundamental problems with such deconvolution methods. One is how to determine the number of bands without resorting to heuristics. The other is difficulty in avoiding the parameter solution trapped into local minima due to the hierarchy and the nonlinearity of the system. In this study, we propose a novel method of spectral deconvolution based on Bayesian estimation with the exchange Monte Carlo method, which is an application of the integral approximation of stochastic complexity and the exchange Monte Carlo method. We also experimentally show its effectiveness on synthetic data and on reflectance spectral data of olivine, one of the most common minerals of terrestrial planets. PMID:22226618

Nagata, Kenji; Sugita, Seiji; Okada, Masato

2012-04-01

207

The Geant4 Virtual Monte Carlo  

NASA Astrophysics Data System (ADS)

The Virtual Monte Carlo (VMC) [1] provides the abstract interface to the Monte Carlo transport codes: GEANT 3.21 [2], Geant4 [3], and FLUKA [4]. The user VMC based application, independent from the specific Monte Carlo codes, can be then run with all supported simulation programs. VMC has been developed by the ALICE Offline Project and it has drawn attention in other experimental frameworks. Since its first release in 2002, the implementation of the VMC for Geant4 (Geant4 VMC) has been continuously maintained and developed, driven by the evolution of Geant4 on one side and the requirements from users on the other side. In this paper we report on new features in this tool, we present its development multi-threading version based on the Geant4 MT prototype [5] as well as the time comparisons of equivalent native Geant4 and VMC test applications.

H?ivná?ová, I.

2012-12-01

208

VERIFICATION OF THE SHIFT MONTE CARLO CODE  

SciTech Connect

Shift is a new hybrid Monte Carlo/deterministic radiation transport code being developed at Oak Ridge National Laboratory. At its current stage of development, Shift includes a fully-functional parallel Monte Carlo capability for simulating eigenvalue and fixed-source multigroup transport problems. This paper focuses on recent efforts to verify Shift s Monte Carlo component using the two-dimensional and three-dimensional C5G7 NEA benchmark problems. Comparisons were made between the benchmark eigenvalues and those output by the Shift code. In addition, mesh-based scalar flux tally results generated by Shift were compared to those obtained using MCNP5 on an identical model and tally grid. The Shift-generated eigenvalues were within three standard deviations of the benchmark and MCNP5 values in all cases. The flux tallies generated by Shift were found to be in very good agreement with those from MCNP

Sly, Nicholas [University of Tennessee, Knoxville (UTK)] [University of Tennessee, Knoxville (UTK); Mervin, Mervin Brenden [University of Tennessee, Knoxville (UTK)] [University of Tennessee, Knoxville (UTK); Mosher, Scott W [ORNL] [ORNL; Evans, Thomas M [ORNL] [ORNL; Wagner, John C [ORNL] [ORNL; Maldonado, G. Ivan [University of Tennessee, Knoxville (UTK)] [University of Tennessee, Knoxville (UTK)

2012-01-01

209

Ejs STP Monte Carlo Pi Model  

NSDL National Science Digital Library

The Monte Carlo Pi program program uses a Monte Carlo routine to approximate pi by approximating the area of a unit circle. The program is distributed as a ready-to-run (compiled) Java archive. Double-clicking the ejs_stp_MonteCarloPi.jar file will run the program if Java is installed on your computer. The program was created using Ejs (Easy Java Simulations). You can modify this program and see how it is designed if you have Ejs installed by right-clicking within the window and selecting Open Ejs Model from the pop-up menu. Ejs, a part of the Open Source Physics Project, is designed to make it easier to access, modify, and generate computer models. Information about Ejs is available at www.um.es/fem/Ejs/. Additional Open Source Physics programs for Statistical and Thermal Physics can be found by searching ComPADRE for Open Source Physics, STP or Statistical and Thermal Physics.

Christian, Wolfgang; Cox, Anne; Gould, Harvey; Tobochnik, Jan

2008-05-31

210

Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application  

Microsoft Academic Search

Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD field in the past two decades for handling NP-hard optimization problems. Recently, a new Monte Carlo and optimization method, named dynamic weighting Monte Carlo (WL97), has been introduced and successfully applied to the traveling salesman problem, neural network training (WL97), and spin-glasses simulation

Jason Cong; Tianming Kong; Faming Liang; Jun S. Liu; Wing Hung Wong; Dongmin Xu

2000-01-01

211

Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application  

Microsoft Academic Search

Simulated annealing has been one of the most popular stochastic optimization methods used in the VLSI CAD field in the past two decades. Recently, a new Monte Carlo and optimization method, named dynamic weighting Monte Carlo [WL97], has been introduced and successfully applied to the traveling salesman problem, neural network training [WL97], and spin-glasses simulation [LW99]. In this paper, we

Jason Cong; Tianming Kong; Faming Liangt; J. S. Liu; Wing Hung Wongt; Dongmin Xu

2000-01-01

212

Fast quantum Monte Carlo on a GPU  

NASA Astrophysics Data System (ADS)

We present a scheme for the parallelization of quantum Monte Carlo method on graphical processing units, focusing on variational Monte Carlo simulation of bosonic systems. We use asynchronous execution schemes with shared memory persistence, and obtain an excellent utilization of the accelerator. The CUDA code is provided along with a package that simulates liquid helium-4. The program was benchmarked on several models of Nvidia GPU, including Fermi GTX560 and M2090, and the Kepler architecture K20 GPU. Special optimization was developed for the Kepler cards, including placement of data structures in the register space of the Kepler GPUs. Kepler-specific optimization is discussed.

Lutsyshyn, Y.

2015-02-01

213

Spin correlations in Monte Carlo simulations.  

E-print Network

preserves the step-by-step approach of the traditional Monte Carlo event generators and a complexity which does not grow exponentially with the number of final-state particles. In this paper we will show that a completely general algorithm can be imple... ar X iv :h ep -p h/ 01 10 10 8v 1 8 O ct 2 00 1 Preprint typeset in JHEP style. - HYPER VERSION Cavendish HEP-2001-13 DAMTP-2001-83 Spin Correlations in Monte Carlo Simulations Peter Richardson Cavendish Laboratory, University of Cambridge...

Richardson, P

214

The Rational Hybrid Monte Carlo algorithm  

NASA Astrophysics Data System (ADS)

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.

Clark, Michael

2006-12-01

215

Geodesic Monte Carlo on Embedded Manifolds  

PubMed Central

Markov chain Monte Carlo methods explicitly defined on the manifold of probability distributions have recently been established. These methods are constructed from diffusions across the manifold and the solution of the equations describing geodesic flows in the Hamilton–Jacobi representation. This paper takes the differential geometric basis of Markov chain Monte Carlo further by considering methods to simulate from probability distributions that themselves are defined on a manifold, with common examples being classes of distributions describing directional statistics. Proposal mechanisms are developed based on the geodesic flows over the manifolds of support for the distributions, and illustrative examples are provided for the hypersphere and Stiefel manifold of orthonormal matrices. PMID:25309024

Byrne, Simon; Girolami, Mark

2013-01-01

216

Monte Carlo inversion of seismic data  

NASA Technical Reports Server (NTRS)

The analytic solution to the linear inverse problem provides estimates of the uncertainty of the solution in terms of standard deviations of corrections to a particular solution, resolution of parameter adjustments, and information distribution among the observations. It is shown that Monte Carlo inversion, when properly executed, can provide all the same kinds of information for nonlinear problems. Proper execution requires a relatively uniform sampling of all possible models. The expense of performing Monte Carlo inversion generally requires strategies to improve the probability of finding passing models. Such strategies can lead to a very strong bias in the distribution of models examined unless great care is taken in their application.

Wiggins, R. A.

1972-01-01

217

The Rational Hybrid Monte Carlo Algorithm  

E-print Network

The past few years have seen considerable progress in algorithmic development for the generation of gauge fields including the effects of dynamical fermions. The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is performed using a rational approximation in place the usual inverse quark matrix kernel is one of these developments. This algorithm has been found to be extremely beneficial in many areas of lattice QCD (chiral fermions, finite temperature, Wilson fermions etc.). We review the algorithm and some of these benefits, and we compare against other recent algorithm developements. We conclude with an update of the Berlin wall plot comparing costs of all popular fermion formulations.

M. A. Clark

2006-10-06

218

Markov Chain Monte Carlo Method without Detailed Balance  

E-print Network

We present a specific algorithm that generally satisfies the balance condition without imposing the detailed balance in the Markov chain Monte Carlo. In our algorithm, the average rejection rate is minimized, and even reduced to zero in many relevant cases. The absence of the detailed balance also introduces a net stochastic flow in a configuration space, which further boosts up the convergence. We demonstrate that the autocorrelation time of the Potts model becomes more than 6 times shorter than that by the conventional Metropolis algorithm. Based on the same concept, a bounce-free worm algorithm for generic quantum spin models is formulated as well.

Hidemaro Suwa; Synge Todo

2010-07-14

219

Monte Carlo event reconstruction implemented with artificial neural networks  

E-print Network

I implemented event reconstruction of a Monte Carlo simulation using neural networks. The OLYMPUS Collaboration is using a Monte Carlo simulation of the OLYMPUS particle detector to evaluate systematics and reconstruct ...

Tolley, Emma Elizabeth

2011-01-01

220

Non-Linear Monte-Carlo Search in Civilization II  

E-print Network

This paper presents a new Monte-Carlo search algorithm for very large sequential decision-making problems. Our approach builds on the recent success of Monte-Carlo tree search algorithms, which estimate the value of states ...

Branavan, Satchuthanan R.

221

Exploring Probability and the Monte Carlo Method  

NSDL National Science Digital Library

This multimedia mathematics resource examines probability. A video illustrates how math is used to evaluate the danger of avalanches in the mountains of Alberta. An interactive component allows students to compare theoretical and experimental probabilities, as well as explore the Monte Carlo method. A probability print activity is also included.

2003-01-01

222

Lexical Frequency Profiles: A Monte Carlo Analysis  

ERIC Educational Resources Information Center

This paper reports a set of Monte Carlo simulations designed to evaluate the main claims made by Laufer and Nation about the Lexical Frequency Profile (LFP). Laufer and Nation claim that the LFP is a sensitive and reliable tool for assessing productive vocabulary in L2 speakers, and they suggest it might have a serious role to play in diagnostic…

Meara, Paul

2005-01-01

223

Physically Based Rendering Monte Carlo Integration  

E-print Network

Physically Based Rendering (600.657) Monte Carlo Integration #12;Probability Definition, and f2 and constant c: ][)()()()( fcEdxxpxfcdxxpxcfcfE pp cdxxpcdxxcpcEp )()( #12;Probability Note: For functions f, f1, and f2 and constant c: 21 212121 )()()()()()()( fEfE

Kazhdan, Michael

224

Monte Carlo Variance of Scrambled Net Quadrature  

Microsoft Academic Search

Hybrids of equidistribution and Monte Carlo methods of integration can achieve the superior accuracy of the former while allowing the simple error estimation methods of the latter. This paper studies the variance of one such hybrid, scrambled nets, by applying a multidimensional multiresolution (wavelet) analysis to the integrand. The integrand is assumed to be measurable and square integrable but not

Art B. Owen

1997-01-01

225

Extended state-space Monte Carlo methods  

Microsoft Academic Search

In this paper various extensions of the parallel-tempering algorithm are developed and their properties are analyzed. The algorithms are designed to alleviate quasiergodic sampling in systems which have rough energy landscapes by coupling individual Monte Carlo chains to form a composite chain. As with parallel tempering, the procedures are based upon extending the state space to include parameters to encourage

Sheldon B. Opps; Jeremy Schofield

2001-01-01

226

Path integral Monte Carlo applied to vortices  

Microsoft Academic Search

The thermodynamic properties of the vortex lattice in a high-Tc superconductor can be understood by considering a system of bosons in two dimensions. In this picture, the imaginary time world-line of the boson corresponds to the vortex. The vortex lattice melting transition maps to melting the Bose Wigner crystal by means of increasing hbar. We use Path Integral Monte Carlo

Henrik Nordborg; Gianni Blatter

1996-01-01

227

Convergence of Sequential Monte Carlo Methods  

Microsoft Academic Search

Bayesian estimation problems where the posterior distribution evolves over time through the accumulationof data arise in many applications in statistics and related fields. Recently, a large number of algorithmsand applications based on sequential Monte Carlo methods (also known as particle filtering methods) haveappeared in the literature to solve this class of problems; see (Doucet, de Freitas & Gordon, 2001) for

Dan Crisan; Arnaud Doucet

2000-01-01

228

Monte Carlo simulation for radiative kaon decays  

E-print Network

For high precision measurements of K decays, the presence of radiated photons cannot be neglected. The Monte Carlo simulations must include the radiative corrections in order to compute the correct event counting and efficiency calculations. In this paper we briefly describe a method for simulating such decays.

C. Gatti

2005-07-26

229

Nonuniversal critical dynamics in Monte Carlo simulations  

Microsoft Academic Search

A new approach to Monte Carlo simulations is presented, giving a highly efficient method of simulation for large systems near criticality. The algorithm violates dynamic universality at second-order phase transitions, producing unusually small values of the dynamical critical exponent.

Robert H. Swendsen; Jian-Sheng Wang

1987-01-01

230

Sequential Monte Carlo Methods for Dynamic Systems  

Microsoft Academic Search

We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they

Jun S. Liu; Rong Chen

1998-01-01

231

Monte Carlo Tools for Jet Quenching  

E-print Network

A thorough understanding of jet quenching on the basis of multi-particle final states and jet observables requires new theoretical tools. This talk summarises the status and propects of the theoretical description of jet quenching in terms of Monte Carlo generators.

Korinna Zapp

2011-09-07

232

Monte Carlo analysis of CLAS data  

E-print Network

We present a fit of the virtual-photon scattering asymmetry of polarized Deep Inelastic Scattering which combines a Monte Carlo technique with the use of a redundant parametrization based on Neural Networks. We apply the result to the analysis of CLAS data on a polarized proton target.

L. Del Debbio; A. Guffanti; A. Piccione

2008-06-30

233

MIMO detection employing Markov Chain Monte Carlo  

E-print Network

We propose a soft-output detection scheme for Multiple-Input-Multiple-Output (MIMO) systems. The detector employs Markov Chain Monte Carlo method to compute bit reliabilities from the signals received and is thus suited for coded MIMO systems. It offers a good trade-off between achievable performance and algorithmic complexity.

V. Sundaram; K. P. N. Murthy

2007-05-05

234

Monte Carlo approach to Dark Matter Mapping  

Microsoft Academic Search

We present an an analysis method of constructing dark matter maps based on weak lensing using a Markov Chain Monte Carlo technique. The dark matter in a cluster can be modeled as a collection of massive blobs that bend light according to gravitational lensing. We move these dark matter blobs in RA, Dec and redshift and as a result perturb

Suzanne Lorenz; J. R. Peterson

2011-01-01

235

Robust Monte Carlo localization for mobile robots  

Microsoft Academic Search

Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL

Sebastian Thrun; Dieter Fox; Wolfram Burgard; Frank Dellaert

2001-01-01

236

Structural Reliability and Monte Carlo Simulation.  

ERIC Educational Resources Information Center

Analyzes a simple boom structure and assesses its reliability using elementary engineering mechanics. Demonstrates the power and utility of Monte-Carlo simulation by showing that such a simulation can be implemented more readily with results that compare favorably to the theoretical calculations. (Author/MM)

Laumakis, P. J.; Harlow, G.

2002-01-01

237

Alternative sampling for variational quantum Monte Carlo.  

PubMed

Expectation values of physical quantities may accurately be obtained by the evaluation of integrals within many-body quantum mechanics, and these multidimensional integrals may be estimated using Monte Carlo methods. In a previous publication it has been shown that for the simplest, most commonly applied strategy in continuum quantum Monte Carlo, the random error in the resulting estimates is not well controlled. At best the central limit theorem is valid in its weakest form, and at worst it is invalid and replaced by an alternative generalized central limit theorem and non-normal random error. In both cases the random error is not controlled. Here we consider a new "residual sampling strategy" that reintroduces the central limit theorem in its strongest form, and provides full control of the random error in estimates. Estimates of the total energy and the variance of the local energy within variational Monte Carlo are considered in detail, and the approach presented may be generalized to expectation values of other operators, and to other variants of the quantum Monte Carlo method. PMID:18351957

Trail, J R

2008-01-01

238

Monte-Carlo Studies of Perovskite Multiferroics  

Microsoft Academic Search

We perform Monte-Carlo simulations of a realistic model of the perovskite multiferroic manganites RMnO3 (R = Gd, Tb, Dy) in order to obtain the ground state phase diagram. It is shown that the dynamic Dzyaloshinskii-Moriya interaction plays a crucial role in stabilizing the state with coexisting incommensurate magnetic and ferroelectric order.

I. A. Sergienko; E. Dagotto

2006-01-01

239

Monte-Carlo Studies of Perovskite Multiferroics  

NASA Astrophysics Data System (ADS)

We perform Monte-Carlo simulations of a realistic model of the perovskite multiferroic manganites RMnO3 (R = Gd, Tb, Dy) in order to obtain the ground state phase diagram. It is shown that the dynamic Dzyaloshinskii-Moriya interaction plays a crucial role in stabilizing the state with coexisting incommensurate magnetic and ferroelectric order.

Sergienko, I. A.; Dagotto, E.

2006-09-01

240

Monte Carlo Renormalization Group: a review  

SciTech Connect

The logic and the methods of Monte Carlo Renormalization Group (MCRG) are reviewed. A status report of results for 4-dimensional lattice gauge theories derived using MCRG is presented. Existing methods for calculating the improved action are reviewed and evaluated. The Gupta-Cordery improved MCRG method is described and compared with the standard one. 71 refs., 8 figs.

Gupta, R.

1985-01-01

241

A quasi-Monte Carlo Metropolis algorithm  

PubMed Central

This work presents a version of the Metropolis–Hastings algorithm using quasi-Monte Carlo inputs. We prove that the method yields consistent estimates in some problems with finite state spaces and completely uniformly distributed inputs. In some numerical examples, the proposed method is much more accurate than ordinary Metropolis–Hastings sampling. PMID:15956207

Owen, Art B.; Tribble, Seth D.

2005-01-01

242

Monte Carlo simulations of dense quantum plasmas  

Microsoft Academic Search

Thermodynamic properties of the equilibrium strongly coupled quantum plasmas investigated by direct path integral Monte Carlo (DPIMC) simulations within a wide region of density, temperature and positive to negative particle mass ratio. Pair distribution functions (PDF), equation of state (EOS), internal energy and Hugoniot are compared with available theoretical and experimental results. Possibilities of the phase transition in hydrogen and

V. S. Filinov; M. Bonitz; V. E. Fortov; W. Ebeling; H. Fehske; D. Kremp; W. D. Kraeft; V. Bezkrovniy; P. Levashov

2006-01-01

243

Monte Carlo Methods in Lattice Gauge Theories  

Microsoft Academic Search

In this work, we study various Monte Carlo methods for lattice gauge theories. The mass of the 0('+) glueball for SU(2) gauge theory in 4 dimensions is calculated. This computation was done on a prototype parallel processor and the implementation of gauge theories on this system is described in detail. Using an action of the purely Wilson form (trace of

Steve William Otto

1983-01-01

244

Monte Carlo simulation techniques for probabilistic tracking  

Microsoft Academic Search

Two novel approaches to probabilistic tracking using Monte Carlo simulation are presented. The first approach is a 3D shape encoded object tracking algorithm. The measurements are derived using the outputs of shape-encoded filters. The nonlinear state estimation is performed by solving the Zakai equation and we use the branching particle propagation method for computing the solution. The second approach is

Baoxin Li; Rama Chellappa; Hankyu Moon

2001-01-01

245

A stochastic Monte Carlo approach to modelling real star cluster evolution - III. Direct integration of three- and four-body interactions  

NASA Astrophysics Data System (ADS)

Spherically symmetric equal-mass star clusters containing a large number of primordial binaries are studied using a hybrid method, consisting of a gas dynamical model for single stars and a Monte Carlo treatment for relaxation of binaries and the setup of close resonant and fly-by encounters of single stars with binaries and binaries with each other (three- and four-body encounters). What differs from our previous work is that each encounter is being integrated using a highly accurate direct few-body integrator which uses regularized variables. Hence we can study the systematic evolution of individual binary orbital parameters (eccentricity, semimajor axis) and differential and total cross-sections for hardening, dissolution or merging of binaries (minimum distance) from a sampling of several tens of thousands of scattering events as they occur in real cluster evolution, including mass segregation of binaries, gravothermal collapse and re-expansion, a binary burning phase and ultimately gravothermal oscillations. For the first time we are able to present empirical cross-sections for eccentricity variation of binaries in close three- and four-body encounters. It is found that a large fraction of three- and four-body encounters result in merging. Eccentricities are generally increased in strong three- and four-body encounters and there is a characteristic scaling law ~ exp (4efin) of the differential cross-section for eccentricity changes, where efin is the final eccentricity of the binary, or harder binary for four-body encounters. Despite these findings the overall eccentricity distribution remains thermal for all binding energies of binaries, which is understood from the dominant influence of resonant encounters. Previous cross-sections obtained by Spitzer and Gao for strong encounters can be reproduced, while for weak encounters non-standard processes such as the formation of hierarchical triples occur.

Giersz, M.; Spurzem, R.

2003-08-01

246

Population Monte Carlo and adaptive sampling schemes Population Monte Carlo and adaptive sampling  

E-print Network

in simulation Target Monte Carlo basics MCMC MCMC difficulties Importance Sampling Pros & cons 2 Population exploration of the space · posterior-ratio acceptance probability · only requires a scale but does require

Robert, Christian P.

247

Parallel Monte-Carlo Tree Search with Simulation Servers  

Microsoft Academic Search

Monte-Carlo tree search is a new best-first tree search algorithm that triggered a revolution in the computer Go world. Developing good parallel Monte-Carlo tree search algorithms is importan because single processor's performance cannot be expected to increase as used to. A novel parallel Monte-Carlo tree search algorithm is proposed. A tree searcher runs on a client computer and multiple Monte-Carlo

Hideki Kato; Ikuo Takeuchi

2010-01-01

248

A Comparison of Monte-Carlo Methods for Phantom Go  

Microsoft Academic Search

Throughout recent years, Monte-Carlo methods have considerably improved computer Go pro- grams. In particular, Monte-Carlo Tree Search algorithms such as UCT have enabled significant advances in this domain. Phantom Go is a variant of Go which is complicated by the condi- tion of imperfect information. This article compares four Monte-Carlo methods for Phantom Go in a self-play experiment: (1) Monte-Carlo

Joris Borsboom; Jahn-Takeshi Saito; Guillaume Chaslot; Jos W. H. M. Uiterwijk

249

Advanced topics 5.1 Hybrid Monte Carlo  

E-print Network

5 Advanced topics 5.1 Hybrid Monte Carlo 5.1.1 The Method One drawback of traditional Monte-Carlo in a Monte-Carlo procedure. See S. Duane, A.D. Kennedy, B.J. Pendleton and D. Roweth, Phys. Lett. B 45, 216;5.1. HYBRID MONTE CARLO 89 · Claim: The transition probability Eq. (5.3) satisfies the stationarity condition

Schofield, Jeremy

250

Smart detectors for Monte Carlo radiative transfer  

E-print Network

Many optimization techniques have been invented to reduce the noise that is inherent in Monte Carlo radiative transfer simulations. As the typical detectors used in Monte Carlo simulations do not take into account all the information contained in the impacting photon packages, there is still room to optimize this detection process and the corresponding estimate of the surface brightness distributions. We want to investigate how all the information contained in the distribution of impacting photon packages can be optimally used to decrease the noise in the surface brightness distributions and hence to increase the efficiency of Monte Carlo radiative transfer simulations. We demonstrate that the estimate of the surface brightness distribution in a Monte Carlo radiative transfer simulation is similar to the estimate of the density distribution in an SPH simulation. Based on this similarity, a recipe is constructed for smart detectors that take full advantage of the exact location of the impact of the photon packages. Several types of smart detectors, each corresponding to a different smoothing kernel, are presented. We show that smart detectors, while preserving the same effective resolution, reduce the noise in the surface brightness distributions compared to the classical detectors. The most efficient smart detector realizes a noise reduction of about 10%, which corresponds to a reduction of the required number of photon packages (i.e. a reduction of the simulation run time) of 20%. As the practical implementation of the smart detectors is straightforward and the additional computational cost is completely negligible, we recommend the use of smart detectors in Monte Carlo radiative transfer simulations.

Maarten Baes

2008-09-11

251

Smart detectors for Monte Carlo radiative transfer  

NASA Astrophysics Data System (ADS)

Many optimization techniques have been invented to reduce the noise that is inherent in Monte Carlo radiative transfer simulations. As the typical detectors used in Monte Carlo simulations do not take into account all the information contained in the impacting photon packages, there is still room to optimize this detection process and the corresponding estimate of the surface brightness distributions. We want to investigate how all the information contained in the distribution of impacting photon packages can be optimally used to decrease the noise in the surface brightness distributions and hence to increase the efficiency of Monte Carlo radiative transfer simulations. We demonstrate that the estimate of the surface brightness distribution in a Monte Carlo radiative transfer simulation is similar to the estimate of the density distribution in a smoothed particle hydrodynamics simulation. Based on this similarity, a recipe is constructed for smart detectors that take full advantage of the exact location of the impact of the photon packages. Several types of smart detectors, each corresponding to a different smoothing kernel, are presented. We show that smart detectors, while preserving the same effective resolution, reduce the noise in the surface brightness distributions compared to the classical detectors. The most efficient smart detector realizes a noise reduction of about 10 per cent, which corresponds to a reduction of the required number of photon packages (i.e. a reduction of the simulation run time) of 20 per cent. As the practical implementation of the smart detectors is straightforward and the additional computational cost is completely negligible, we recommend the use of smart detectors in Monte Carlo radiative transfer simulations.

Baes, Maarten

2008-12-01

252

Reweighting in Monte Carlo and Monte Carlo renormalization-group studies  

Microsoft Academic Search

A method of reweighting Monte Carlo data to parameters other than the simulated ones is investigated. The method is also extended to include Monte Carlo renormalization-group simulations. Both single and multiple simulation reweighting is used. The models studied are the {ital d}=2 ferromagnetic {ital q}=3 Potts model and the {ital d}=2 antiferromagnetic Ising model. It is shown that the reweighting

E. P. Muenger; M. A. Novotny

1991-01-01

253

Reweighting in Monte Carlo and Monte Carlo renormalization-group studies  

Microsoft Academic Search

A method of reweighting Monte Carlo data to parameters other than the simulated ones is investigated. The method is also extended to include Monte Carlo renormalization-group simulations. Both single and multiple simulation reweighting is used. The models studied are the d=2 ferromagnetic q=3 Potts model and the d=2 antiferromagnetic Ising model. It is shown that the reweighting produces a systematic

E. P. Münger; M. A. Novotny

1991-01-01

254

Markov chain Monte Carlo techniques in iterative detectors: a novel approach based on Monte Carlo integration  

Microsoft Academic Search

We present a novel soft-in soft-out (SISO) detection scheme based on Markov-chain Monte-Carlo (MCMC) simulations. The proposed detector is applicable to both synchronous multiuser and multiple-input multiple-output (MIMO) communication systems. Unlike previous publications on the subject, we use Monte Carlo integration technique to arrive at the receiver structure. The proposed multiuser\\/MIMO detector is found to follow the Rao-Blackwell formulation and

Zhenning Shi; Haidong Zhu; Behrouz Farhang-Boroujeny

2004-01-01

255

SEQUENTIAL MONTE CARLO TECHNIQUES FOR THE SOLUTION OF LINEAR SYSTEMS  

E-print Network

SEQUENTIAL MONTE CARLO TECHNIQUES FOR THE SOLUTION OF LINEAR SYSTEMS by JOHN H. HALTON. They generate the truncated sums xs = r=0 s Hr a. The usual plain Monte Carlo approach uses independent "random. The alternative presented here, is to apply a sequential Monte Carlo method, in which the sampling scheme

North Carolina at Chapel Hill, University of

256

A Practical Monte Carlo Implementation of Bayesian Learning  

E-print Network

A Practical Monte Carlo Implementation of Bayesian Learning Carl Edward Rasmussen Department A practical method for Bayesian training of feed-forward neural networks using sophisticated Monte Carlo of training data. Here I present and test a Monte Carlo method (Neal, 1995) which avoids the Gaussian

Edinburgh, University of

257

Optimally Combining Sampling Techniques for Monte Carlo Rendering  

E-print Network

Optimally Combining Sampling Techniques for Monte Carlo Rendering Eric Veach Leonidas J. Guibas Computer Science Department Stanford University Abstract Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo

Stanford University

258

Simulated Annealing: A Monte Carlo Method for GPS Surveying  

E-print Network

annealing technique,which is a Monte Carlo method, to analyze and improve the e#ciency of the de­ signSimulated Annealing: A Monte Carlo Method for GPS Surveying Stefka Fidanova IPP -- BAS, Acad. G that uses a Monte Carlo global minimization technique for minimizing multi­variance functions [2

Fidanova, Stefka

259

A convergence criterion for the Monte Carlo estimates  

Microsoft Academic Search

In this article, a convergence criterion for the Monte Carlo estimates, which can be used as a stopping rule for the Monte Carlo experiments, will be proposed. The proposed criterion seeks a convergence band of a given width and length such that the probability of the Monte Carlo sample means to fall outside of this band is practically null. Although

Mustafa Y. Ata

2007-01-01

260

Distributionally Robust Monte Carlo Simulation: A Tutorial Survey  

Microsoft Academic Search

Abstract Whereas the use of traditional Monte Carlo simulation requires probability distribu- tions for the uncertain parameters entering the system, distributionally robust Monte Carlo simulation does not. The description of this new approach to Monte Carlo simu- lation is the focal point of this tutorial survey. According to the new theory, instead of carrying out simulations using some rather arbitrary

Constantino M. Lagoa; B. Ross Barmish

2001-01-01

261

Generalised linear mixed model analysis via sequential Monte Carlo sampling  

Microsoft Academic Search

We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general method for

Y. Fan; D. S. Leslie; M. P. Wand

2008-01-01

262

Kernel density estimator methods for Monte Carlo radiation transport  

Microsoft Academic Search

In this dissertation, the Kernel Density Estimator (KDE), a nonparametric probability density estimator, is studied and used to represent global Monte Carlo (MC) tallies. KDE is also employed to remove the singularities from two important Monte Carlo tallies, namely point detector and surface crossing flux tallies. Finally, KDE is also applied to accelerate the Monte Carlo fission source iteration for

Kaushik Banerjee

2010-01-01

263

Optimal Design via Curve Fitting of Monte Carlo Experiments  

E-print Network

Optimal Design via Curve Fitting of Monte Carlo Experiments Peter M¨ uller and Giovanni Parmigiani : : : : : : : : : : : 15 4.3.3 Curve fitting of Monte Carlo experiments : : : : : : : : 16 5 CONSISTENCY AND UNCERTAINTY 18 approximation. : : : : : : : : : : : : : : : 14 4.3.2 Large scale Monte Carlo integration

West, Mike

264

MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS  

E-print Network

MONTE CARLO LIKELIHOOD INFERENCE FOR MISSING DATA MODELS By Yun Ju Sung and Charles J. Geyer University of Washington and University of Minnesota Abbreviated title: Monte Carlo Likelihood Asymptotics We describe a Monte Carlo method to approximate the maximum likeli- hood estimate (MLE), when

Jiang, Tiefeng

265

A Monte Carlo Approach for Finding More than One Eigenpair  

E-print Network

A Monte Carlo Approach for Finding More than One Eigenpair Michael Mascagni1 and Aneta Karaivanova1. 25A, 1113 Sofia, Bulgaria, aneta@csit.fsu.edu, http://parallel.bas.bg/anet/ Abstract. The Monte Carlo) eigenvalues of matrices. In this paper we study computing eigenvectors as well with the Monte Carlo approach

Karaivanova, Aneta

266

Monte Carlo Methods: A Computational Pattern for Our Pattern Language  

E-print Network

Monte Carlo Methods: A Computational Pattern for Our Pattern Language Jike Chong University@eecs.berkeley.edu Kurt Keutzer University of California, Berkeley keutzer@eecs.berkeley.edu ABSTRACT The Monte Carlo for a particular data working set. This paper presents the Monte Carlo Methods software pro- gramming pattern

California at Berkeley, University of

267

Monte Carlo Algorithms for the Partition Function and Information Rates  

E-print Network

1 Monte Carlo Algorithms for the Partition Function and Information Rates of Two Monte Carlo algorithms for the computation of the information rate of two-dimensional source / channel, of such channels has so far remained largely unsolved. Both problems can be reduced to computing a Monte Carlo

Loeliger, Hans-Andrea

268

Monte Carlo Evaluation of Resampling-Based Hypothesis Tests  

E-print Network

Monte Carlo Evaluation of Resampling-Based Hypothesis Tests Dennis D. Boos and Ji Zhang October 1998 Abstract Monte Carlo estimation of the power of tests that require resampling can be very com in correcting for bias and thus reduces computation time in Monte Carlo power studies. KEY WORDS: Bootstrap

Boos, Dennis

269

MONTE CARLO METHODS IN GEOPHYSICAL INVERSE Malcolm Sambridge  

E-print Network

MONTE CARLO METHODS IN GEOPHYSICAL INVERSE PROBLEMS Malcolm Sambridge Research School of Earth 2002. [1] Monte Carlo inversion techniques were first used by Earth scientists more than 30 years ago in exploration seismology. This pa- per traces the development and application of Monte Carlo methods for inverse

Sambridge, Malcolm

270

MONTE CARLO ANALYSIS: ESTIMATING GPP WITH THE CANOPY CONDUCTANCE METHOD  

E-print Network

MONTE CARLO ANALYSIS: ESTIMATING GPP WITH THE CANOPY CONDUCTANCE METHOD 1. Overview A novel method performed a Monte Carlo Analysis to investigate the power of our statistical approach: i.e. what and Assumptions The Monte Carlo Analysis was performed as follows: · Natural variation. The only study to date

DeLucia, Evan H.

271

MONTE CARLO SIMULATION FOR AMERICAN Russel E. Caflisch  

E-print Network

#12;Chapter 1 MONTE CARLO SIMULATION FOR AMERICAN OPTIONS Russel E. Caflisch Mathematics Department This paper reviews the basic properties of American options and the difficulties of applying Monte Carlo of Monte Carlo to American options is described including the following: Branching processes have been con

Caflisch, Russel

272

Monte Carlo Reliability Model for Microwave Monolithic Integrated Circuits  

E-print Network

Monte Carlo Reliability Model for Microwave Monolithic Integrated Circuits Aris Christou Materials of the failure rate of each component due to interaction effects of the failed components. The Monte Carlo failure rates become nonconstant. The Monte Carlo technique is an appropriate methodology used to treat

Rubloff, Gary W.

273

Monte Carlo Ray Tracing Siggraph 2003 Course 44  

E-print Network

Monte Carlo Ray Tracing Siggraph 2003 Course 44 Tuesday, July 29, 2003 Organizer Henrik Wann Jensen;Abstract This full day course will provide a detailed overview of state of the art in Monte Carlo ray tracing. Recent advances in algorithms and available compute power have made Monte Carlo ray tracing based

Li, Yaohang

274

Monte Carlo simulations and option by Bingqian Lu  

E-print Network

Monte Carlo simulations and option pricing by Bingqian Lu Undergraduate Mathematics Department #12;Abstract Monte Carlo simulation is a legitimate and widely used technique for dealing of this technique to the stock volality and to test its accuracy by comparing the result computed by Monte Carlo

Mazzucato, Anna

275

Monte Carlo modeling of optical coherence tomography systems  

E-print Network

Monte Carlo modeling of optical coherence tomography systems Peter E. Andersen Optics and Fluid 2003 Outline · Motivation · Monte Carlo OCT ­ use MC to model interference? · Results ­ comparison Dynamics Department SFM'03 ­ 7-10 October 2003 Motivation · Monte Carlo (MC) modeling of light propagation

276

Monte-Carlo Tree Search in Crazy Stone Remi Coulom  

E-print Network

Monte-Carlo Tree Search in Crazy Stone R´emi Coulom Universit´e Charles de Gaulle, INRIA, CNRS Introduction 2 Crazy Stone's Algorithm Principles of Monte-Carlo Evaluation Tree Search Patterns 3 Playing global understanding The Monte-Carlo Approach random playouts dynamic evaluation with global

Coulom, Rémi - Groupe de Recherche sur l'Apprentissage Automatique, Université Charles de Gaulle

277

Monte Carlo procedure for protein design Anders Irback,* Carsten Peterson,  

E-print Network

Monte Carlo procedure for protein design Anders Irba¨ck,* Carsten Peterson, Frank Potthast functions, is based upon a different and very efficient multisequence Monte Carlo scheme. By construction a practical Monte Carlo MC procedure for perform- ing the maximization of P(r0 ). Thermodynamical

Irbäck, Anders

278

Sequential Monte Carlo on large binary sampling spaces  

E-print Network

Sequential Monte Carlo on large binary sampling spaces Christian Sch¨afer1,2 Nicolas Chopin1,3 November 8, 2011 A Monte Carlo algorithm is said to be adaptive if it automatically cali- brates its distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions

Paris-Sud XI, Université de

279

Use of Monte Carlo Analysis to Characterize Nitrogen Fluxes in  

E-print Network

Use of Monte Carlo Analysis to Characterize Nitrogen Fluxes in Agroecosystems S H E L I E A . M I L systems, this paper employs Monte Carlo analysis (MCA) to model major nitrogen exports during crop assessments (LCA) and environmental impact assessments. Monte Carlo simulations generate distributions

Illinois at Chicago, University of

280

Smart Monte Carlo for Yield Estimation Serdar Tasiran Alper Demir  

E-print Network

Smart Monte Carlo for Yield Estimation Serdar Tasiran Alper Demir stasiran@ku.edu.tr aldemir and compu- tationally viable estimation of timing yield using circuit-level Monte Carlo simulation. Our techniques are based on well-known variance reduction approaches from Monte Carlo simulation literature

Tasiran, Serdar

281

Sequential Monte Carlo Methods Nando De Freitas & Arnaud Doucet  

E-print Network

Sequential Monte Carlo Methods Nando De Freitas & Arnaud Doucet UBC Nando De Freitas & Arnaud Doucet UBC ( ) Sequential Monte Carlo Methods 1 / 39 #12;State-Space Models fXk gk 1 hidden X -valued ( ) Sequential Monte Carlo Methods 2 / 39 #12;State-Space Models fXk gk 1 hidden X -valued Markov process with X1

Doucet, Arnaud

282

Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo  

E-print Network

Biopolymer structure simulation and optimization via fragment regrowth Monte Carlo Jinfeng Zhang, S. In this study, the authors propose a new Monte Carlo method, fragment regrowth via energy-guided sequential sampling FRESS , which incorporates the idea of multigrid Monte Carlo into the framework of configurational

McQuade, D. Tyler

283

Parallel Monte Carlo Approach for Integration of the Rendering Equation  

E-print Network

Parallel Monte Carlo Approach for Integration of the Rendering Equation Ivan T. Dimov1 , Anton A are the Monte Carlo methods for solving the rendering equation in order to create photorealistic images. In this work we consider the Monte Carlo solving of the render- ing equation in the context of the parallel

Dimov, Ivan

284

Bayesian Monte Carlo Carl Edward Rasmussen and Zoubin Ghahramani  

E-print Network

Bayesian Monte Carlo Carl Edward Rasmussen and Zoubin Ghahramani Gatsby Computational Neuroscience,zoubin@gatsby.ucl.ac.uk http://www.gatsby.ucl.ac.uk Abstract We investigate Bayesian alternatives to classical Monte Carlo methods for evaluating integrals. Bayesian Monte Carlo (BMC) allows the in- corporation of prior knowledge

Ghahramani, Zoubin

285

Parallel Monte Carlo Ion Recombination Simulation in Orca  

E-print Network

Parallel Monte Carlo Ion Recombination Simulation in Orca Frank J. Seinstra Department. This report describes the implementation in Orca of a realistic Monte Carlo simulation of the recombinations. Keywords: Parallel computing, Orca, Ethernet, Myrinet, Monte Carlo sim­ ulation, ion recombination

Seinstra, Frank J.

286

STORM in Monte Carlo reactor physics calculations KAUR TUTTELBERG  

E-print Network

STORM in Monte Carlo reactor physics calculations KAUR TUTTELBERG Master of Science Thesis by the industry's needs for efficient Monte Carlo criticality solvers with advanced error estimation routines of the errors in the cumulative fission source is crucial for correct interpretation of results from Monte Carlo

Haviland, David

287

Biasing Monte-Carlo Simulations through RAVE Arpad Rimmel1  

E-print Network

Biasing Monte-Carlo Simulations through RAVE Values Arpad Rimmel1 , Fabien Teytaud2 , and Olivier. The Monte-Carlo Tree Search algorithm has been success- fully applied in various domains. However, its performance heavily de- pends on the Monte-Carlo part. In this paper, we propose a generic way of improving

Paris-Sud XI, Université de

288

Kinetic Monte Carlo approach to modeling dislocation mobility  

E-print Network

Kinetic Monte Carlo approach to modeling dislocation mobility Wei Cai a , Vasily V. Bulatov b , Jo of the experimental data. Ã? 2002 Elsevier Science B.V. All rights reserved. Keywords: Dislocation; Kinetic Monte Carlo; Silicon; BCC Metals 1. Introduction Kinetic Monte Carlo (kMC) method is generally used to simulate

Cai, Wei

289

Low energy photon dosimetry using Monte Carlo and convolution methods  

Microsoft Academic Search

Low energy photon dosimetry was investigated using Monte Carlo and convolution methods. Photon energy deposition kernels describing the three dimensional distribution of energy deposition about a primary photon interaction site were computed using EGS4 Monte Carlo. These photon energy deposition kernels were utilized as the convolution kernel in convolution\\/superposition dose calculations. A Monte Carlo bench mark describing the energy deposition

Joseph Michael Modrick

2000-01-01

290

John von Neumann Institute for Computing Monte Carlo Protein Folding  

E-print Network

John von Neumann Institute for Computing Monte Carlo Protein Folding: Simulations of Met://www.fz-juelich.de/nic-series/volume20 #12;#12;Monte Carlo Protein Folding: Simulations of Met-Enkephalin with Solvent-Accessible Area difficulties in applying Monte Carlo methods to protein folding. The solvent-accessible area method, a popular

Hsu, Hsiao-Ping

291

American Monte Carlo Option Pricing under Pure Jump Levy Models  

E-print Network

American Monte Carlo Option Pricing under Pure Jump L´evy Models Lydia West Thesis presented Stellenbosch University All rights reserved i #12;Abstract We study Monte Carlo methods for pricing American Monte Carlo methods. We then consider two classes of these methods. The first class involves regression

Ouwehand, Peter

292

Quasi-Monte Carlo Estimation in Generalized Linear Mixed Models  

E-print Network

Quasi-Monte Carlo Estimation in Generalized Linear Mixed Models J. Pan & R. Thompson First version, The University of Manchester #12;Quasi-Monte Carlo Estimation in Generalized Linear Mixed Models Jianxin Pan a-dimensional random effects. Based on the Quasi-Monte Carlo (QMC) approx- imation, a heuristic approach is proposed

Sidorov, Nikita

293

Adaptive Monte Carlo on multivariate binary sampling spaces  

E-print Network

Adaptive Monte Carlo on multivariate binary sampling spaces Christian Sch¨afer , Nicolas Chopin A Monte Carlo algorithm is said to be adaptive if it can adjust automatically its current proposal binary families, to make adaptive Monte Carlo procedures efficient. Besides, our numerical results

294

ENVIRONMENTAL MODELING: 1 APPLICATIONS: MONTE CARLO SENSITIVITY SIMULATIONS  

E-print Network

are sensitive to small variances of the rate constant. Second, the developed Monte Carlo simulation techniqueENVIRONMENTAL MODELING: case study Ivan Dimov #12;Contents 1 APPLICATIONS: MONTE CARLO SENSITIVITY . . . . . . . . . . . . . . . . . . . . . . . 6 1.2 An Algorithm for Monte Carlo Simulation . . . . . . . . . . . . . . . 7 1.3 Finding

Dimov, Ivan

295

Monte Carlo methods for design and analysis of radiation detectors  

Microsoft Academic Search

An overview of Monte Carlo as a practical method for designing and analyzing radiation detectors is provided. The emphasis is on detectors for radiation that is either directly or indirectly ionizing. This overview paper reviews some of the fundamental aspects of Monte Carlo, briefly addresses simulation of radiation transport by the Monte Carlo method, discusses the differences between direct and

William L. Dunn; J. Kenneth Shultis

2009-01-01

296

Coupled Electron Ion Monte Carlo Calculations of Atomic Markus Holzmann a, Carlo Pierleoni b  

E-print Network

Coupled Electron Ion Monte Carlo Calculations of Atomic Hydrogen Markus Holzmann a, Carlo Pierleoni, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA Abstract We present a new Monte Carlo method which couples Path Integral for finite temperature protons with Quantum Monte Carlo for ground

297

New variational Monte Carlo method with an energy variance extrapolation for large-scale shell-model calculations  

E-print Network

We propose a new variational Monte Carlo (VMC) method with an energy variance extrapolation for large-scale shell-model calculations. This variational Monte Carlo is a stochastic optimization method with a projected correlated condensed pair state as a trial wave function, and is formulated with the M-scheme representation of projection operators, the Pfaffian and the Markov-chain Monte Carlo (MCMC). Using this method, we can stochastically calculate approximated yrast energies and electro-magnetic transition strengths. Furthermore, by combining this VMC method with energy variance extrapolation, we can estimate exact shell-model energies.

Takahiro Mizusaki; Noritaka Shimizu

2012-01-27

298

Markov Chain Monte-Carlo Models of Starburst Clusters  

NASA Astrophysics Data System (ADS)

There are a number of stochastic effects that must be considered when comparing models to observations of starburst clusters: the IMF is never fully populated; the stars can never be strictly coeval; stars rotate and their photometric properties depend on orientation; a significant fraction of massive stars are in interacting binaries; and the extinction varies from star to star. The probability distributions of each of these effects are not a priori known, but must be extracted from the observations. Markov Chain Monte-Carlo methods appear to provide the best statistical approach. Here I present an example of stochastic age effects upon the upper mass limit of the IMF of the Arches cluster as derived from near-IR photometry.

Melnick, Jorge

2015-01-01

299

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing  

NASA Astrophysics Data System (ADS)

This paper proposes a comprehensive framework for accelerating population balance-Monte Carlo (PBMC) simulation of particle coagulation dynamics. By combining Markov jump model, weighted majorant kernel and GPU (graphics processing unit) parallel computing, a significant gain in computational efficiency is achieved. The Markov jump model constructs a coagulation-rule matrix of differentially-weighted simulation particles, so as to capture the time evolution of particle size distribution with low statistical noise over the full size range and as far as possible to reduce the number of time loopings. Here three coagulation rules are highlighted and it is found that constructing appropriate coagulation rule provides a route to attain the compromise between accuracy and cost of PBMC methods. Further, in order to avoid double looping over all simulation particles when considering the two-particle events (typically, particle coagulation), the weighted majorant kernel is introduced to estimate the maximum coagulation rates being used for acceptance-rejection processes by single-looping over all particles, and meanwhile the mean time-step of coagulation event is estimated by summing the coagulation kernels of rejected and accepted particle pairs. The computational load of these fast differentially-weighted PBMC simulations (based on the Markov jump model) is reduced greatly to be proportional to the number of simulation particles in a zero-dimensional system (single cell). Finally, for a spatially inhomogeneous multi-dimensional (multi-cell) simulation, the proposed fast PBMC is performed in each cell, and multiple cells are parallel processed by multi-cores on a GPU that can implement the massively threaded data-parallel tasks to obtain remarkable speedup ratio (comparing with CPU computation, the speedup ratio of GPU parallel computing is as high as 200 in a case of 100 cells with 10 000 simulation particles per cell). These accelerating approaches of PBMC are demonstrated in a physically realistic Brownian coagulation case. The computational accuracy is validated with benchmark solution of discrete-sectional method. The simulation results show that the comprehensive approach can attain very favorable improvement in cost without sacrificing computational accuracy.

Xu, Zuwei; Zhao, Haibo; Zheng, Chuguang

2015-01-01

300

Improved diffusion Monte Carlo and the Brownian fan  

NASA Astrophysics Data System (ADS)

Diffusion Monte Carlo (DMC) is a workhorse of stochastic computing. It was invented forty years ago as the central component in a Monte Carlo technique for estimating various characteristics of quantum mechanical systems. Since then it has been used in applied in a huge number of fields, often as a central component in sequential Monte Carlo techniques (e.g. the particle filter). DMC computes averages of some underlying stochastic dynamics weighted by a functional of the path of the process. The weight functional could represent the potential term in a Feynman-Kac representation of a partial differential equation (as in quantum Monte Carlo) or it could represent the likelihood of a sequence of noisy observations of the underlying system (as in particle filtering). DMC alternates between an evolution step in which a collection of samples of the underlying system are evolved for some short time interval, and a branching step in which, according to the weight functional, some samples are copied and some samples are eliminated. Unfortunately for certain choices of the weight functional DMC fails to have a meaningful limit as one decreases the evolution time interval between branching steps. We propose a modification of the standard DMC algorithm. The new algorithm has a lower variance per workload, regardless of the regime considered. In particular, it makes it feasible to use DMC in situations where the ``naive'' generalization of the standard algorithm would be impractical, due to an exponential explosion of its variance. We numerically demonstrate the effectiveness of the new algorithm on a standard rare event simulation problem (probability of an unlikely transition in a Lennard-Jones cluster), as well as a high-frequency data assimilation problem. We then provide a detailed heuristic explanation of why, in the case of rare event simulation, the new algorithm is expected to converge to a limiting process as the underlying stepsize goes to 0. This is shown rigorously in the simplest possible situation of a random walk, biased by a linear potential. The resulting limiting object, which we call the ``Brownian fan'', is a very natural new mathematical object of independent interest.The reconstruction (dotted lines) of a trajectory of stochastic Lorenz 63 (solid lines) by DMC (the standard particle filter). The reconstruction by the modified DMC algorithm.

Weare, J.; Hairer, M.

2012-12-01

301

Simple Monte Carlo Integration Importance Sampling The Law of Large Numbers  

E-print Network

Simple Monte Carlo Integration Importance Sampling Topic 10 The Law of Large Numbers Monte Carlo Integration 1 / 13 #12;Simple Monte Carlo Integration Importance Sampling Outline Simple Monte Carlo Integration Importance Sampling 2 / 13 #12;Simple Monte Carlo Integration Importance Sampling Monte Carlo

Watkins, Joseph C.

302

Monte Carlo study of vibrational relaxation processes  

NASA Technical Reports Server (NTRS)

A new model is proposed for the computation of vibrational nonequilibrium in the direct simulation Monte Carlo method (DSMC). This model permits level to level vibrational transitions for the first time in a Monte Carlo flowfield simulation. The model follows the Landau-Teller theory for a harmonic oscillator in which the rates of transition are related to an experimental correlation for the vibrational relaxation time. The usual method for simulating such processes in the DSMC technique applies a constant exchange probability to each collision and the vibrational energy is treated as a continuum. A comparison of these two methods is made for the flow of nitrogen over a wedge. Significant differences exist for the vibrational temperatures computed. These arise as a consequence of the incorrect application of a constant exchange probability in the old method. It is found that the numerical performances of the two vibrational relaxation models are equal.

Boyd, Iain D.

1991-01-01

303

Status of Monte Carlo at Los Alamos  

SciTech Connect

At Los Alamos the early work of Fermi, von Neumann, and Ulam has been developed and supplemented by many followers, notably Cashwell and Everett, and the main product today is the continuous-energy, general-purpose, generalized-geometry, time-dependent, coupled neutron-photon transport code called MCNP. The Los Alamos Monte Carlo research and development effort is concentrated in Group X-6. MCNP treats an arbitrary three-dimensional configuration of arbitrary materials in geometric cells bounded by first- and second-degree surfaces and some fourth-degree surfaces (elliptical tori). Monte Carlo has evolved into perhaps the main method for radiation transport calculations at Los Alamos. MCNP is used in every technical division at the Laboratory by over 130 users about 600 times a month accounting for nearly 200 hours of CDC-7600 time.

Thompson, W.L.; Cashwell, E.D.

1980-01-01

304

Status of Monte Carlo at Los Alamos  

SciTech Connect

Four papers were presented by Group X-6 on April 22, 1980, at the Oak Ridge Radiation Shielding Information Center (RSIC) Seminar-Workshop on Theory and Applications of Monte Carlo Methods. These papers are combined into one report for convenience and because they are related to each other. The first paper (by Thompson and Cashwell) is a general survey about X-6 and MCNP and is an introduction to the other three papers. It can also serve as a resume of X-6. The second paper (by Godfrey) explains some of the details of geometry specification in MCNP. The third paper (by Cashwell and Schrandt) illustrates calculating flux at a point with MCNP; in particular, the once-more-collided flux estimator is demonstrated. Finally, the fourth paper (by Thompson, Deutsch, and Booth) is a tutorial on some variance-reduction techniques. It should be required for a fledging Monte Carlo practitioner.

Thompson, W.L.; Cashwell, E.D.; Godfrey, T.N.K.; Schrandt, R.G.; Deutsch, O.L.; Booth, T.E.

1980-05-01

305

Monte Carlo simulation of gas Cerenkov detectors  

SciTech Connect

Theoretical study of selected gamma-ray and electron diagnostic necessitates coupling Cerenkov radiation to electron/photon cascades. A Cerenkov production model and its incorporation into a general geometry Monte Carlo coupled electron/photon transport code is discussed. A special optical photon ray-trace is implemented using bulk optical properties assigned to each Monte Carlo zone. Good agreement exists between experimental and calculated Cerenkov data in the case of a carbon-dioxide gas Cerenkov detector experiment. Cerenkov production and threshold data are presented for a typical carbon-dioxide gas detector that converts a 16.7 MeV photon source to Cerenkov light, which is collected by optics and detected by a photomultiplier.

Mack, J.M.; Jain, M.; Jordan, T.M.

1984-01-01

306

Monte Carlo Simulation of THz Multipliers  

NASA Technical Reports Server (NTRS)

Schottky Barrier diode frequency multipliers are critical components in submillimeter and Thz space based earth observation systems. As the operating frequency of these multipliers has increased, the agreement between design predictions and experimental results has become poorer. The multiplier design is usually based on a nonlinear model using a form of harmonic balance and a model for the Schottky barrier diode. Conventional voltage dependent lumped element models do a poor job of predicting THz frequency performance. This paper will describe a large signal Monte Carlo simulation of Schottky barrier multipliers. The simulation is a time dependent particle field Monte Carlo simulation with ohmic and Schottky barrier boundary conditions included that has been combined with a fixed point solution for the nonlinear circuit interaction. The results in the paper will point out some important time constants in varactor operation and will describe the effects of current saturation and nonlinear resistances on multiplier operation.

East, J.; Blakey, P.

1997-01-01

307

Quantum Monte Carlo by message passing  

SciTech Connect

We summarize results of quantum Monte Carlo simulations of the degenerate single-impurity Anderson model using the impurity algorithm of Hirsch and Fye. Using methods of Bayesian statistical inference, coupled with the principle of maximum entropy, we extracted the single-particle spectral density from the imaginary-time Green`s function. The variations of resulting spectral densities with model parameters agree qualitatively with the spectral densities predicted by NCA calculations. All the simulations were performed on a cluster of 16 IBM R6000/560 workstations under the control of the message-passing software PVM. We described the trivial parallelization of our quantum Monte Carlo code both for the cluster and the CM-5 computer. Other issues for effective parallelization of the impurity algorithm are also discussed.

Bonca, J.; Gubernatis, J.E.

1993-05-01

308

Monte Carlo Simulations of Ultrathin Magnetic Dots  

E-print Network

In this work we study the thermodynamic properties of ultrathin ferromagnetic dots using Monte Carlo simulations. We investigate the vortex density as a function of the temperature and the vortex structure in monolayer dots with perpendicular anisotropy and long-range dipole interaction. The interplay between these two terms in the hamiltonian leads to an interesting behavior of the thermodynamic quantities as well as the vortex density.

M. Rapini; R. A. Dias; D. P. Landau; B. V. Costa

2006-04-10

309

Monte Carlo simulation of Alaska wolf survival  

NASA Astrophysics Data System (ADS)

Alaskan wolves live in a harsh climate and are hunted intensively. Penna's biological aging code, using Monte Carlo methods, has been adapted to simulate wolf survival. It was run on the case in which hunting causes the disruption of wolves' social structure. Social disruption was shown to increase the number of deaths occurring at a given level of hunting. For high levels of social disruption, the population did not survive.

Feingold, S. J.

1996-02-01

310

Monte-carlo study of LHCb preshower  

E-print Network

Abstract Results of LHCb preshower studies done with stand-alone Monte-Carlo programs are described. The main performances of PreShower detector, such as dynamic range, 7r/e rejection and energy corrections of ECAL response have been estimated for 1.5,2,2.5 arid 3X0 thicknesses of the PS absorber. Effect of light photo-statistics was also considered.

Guschin, E

2000-01-01

311

Monte Carlo Simulations of Star Clusters  

E-print Network

A revision of Stod\\'o{\\l}kiewicz's Monte Carlo code is used to simulate evolution of large star clusters. The survey on the evolution of multi-mass N-body systems influenced by the tidal field of a parent galaxy and by stellar evolution is discussed. For the first time, the simulation on the "star-by-star" bases of evolution of 1,000,000 body star cluster is presented. \\

Mirek Giersz

2000-06-30

312

Collective Monte Carlo updating for spin systems  

Microsoft Academic Search

A Monte Carlo algorithm is presented that updates large clusters of spins simultaneously in systems at and near criticality. We demonstrate its efficiency in the two-dimensional O(n) sigma models for n=1 (Ising) and n=2 (x-y) at their critical temperatures, and for n=3 (Heisenberg) with correlation lengths around 10 and 20. On lattices up to 1282 no sign of critical slowing

Ulli Wolff

1989-01-01

313

Time Series Simulation with Quasi Monte Carlo Methods  

Microsoft Academic Search

This paper compares quasi Monte Carlo methods, in particularso-called (t, m, s)-nets, with classical Monte Carlo approaches forsimulating econometric time-series models. Quasi Monte Carlomethods have found successful application in many fields, such asphysics, image processing, and the evaluation of financederivatives. However, they are rarely used in econometrics. Here,we apply both traditional and quasi Monte Carlo simulation methodsto time-series models that

Jenny X. Li; Peter Winker

2003-01-01

314

Numerical reproducibility for implicit Monte Carlo simulations  

SciTech Connect

We describe and compare different approaches for achieving numerical reproducibility in photon Monte Carlo simulations. Reproducibility is desirable for code verification, testing, and debugging. Parallelism creates a unique problem for achieving reproducibility in Monte Carlo simulations because it changes the order in which values are summed. This is a numerical problem because double precision arithmetic is not associative. In [1], a way of eliminating this roundoff error using integer tallies was described. This approach successfully achieves reproducibility at the cost of lost accuracy by rounding double precision numbers to fewer significant digits. This integer approach, and other extended reproducibility techniques, are described and compared in this work. Increased precision alone is not enough to ensure reproducibility of photon Monte Carlo simulations. A non-arbitrary precision approaches required a varying degree of rounding to achieve reproducibility. For the problems investigated in this work double precision global accuracy was achievable by using 100 bits of precision or greater on all unordered sums which where subsequently rounded to double precision at the end of every time-step. (authors)

Cleveland, M.; Brunner, T.; Gentile, N. [Lawrence Livermore National Laboratory, P. O. Box 808, Livermore CA 94550 (United States)

2013-07-01

315

Monte Carlo dose mapping on deforming anatomy  

PubMed Central

This paper proposes a Monte Carlo-based energy and mass congruent mapping (EMCM) method to calculate the dose on deforming anatomy. Different from dose interpolation methods, EMCM separately maps each voxel’s deposited energy and mass from a source image to a reference image with a displacement vector field (DVF) generated by deformable image registration (DIR). EMCM was compared with other dose mapping methods: energy-based dose interpolation (EBDI) and trilinear dose interpolation (TDI). These methods were implemented in EGSnrc/DOSXYZnrc, validated using a numerical deformable phantom and compared for clinical CT images. On the numerical phantom with an analytically invertible deformation map, EMCM mapped the dose exactly the same as its analytic solution, while EBDI and TDI had average dose errors of 2.5% and 6.0%. For a lung patient’s IMRT treatment plan, EBDI and TDI differed from EMCM by 1.96% and 7.3% in the lung patient’s entire dose region, respectively. As a 4D Monte Carlo dose calculation technique, EMCM is accurate and its speed is comparable to 3D Monte Carlo simulation. This method may serve as a valuable tool for accurate dose accumulation as well as for 4D dosimetry QA. PMID:19741278

Zhong, Hualiang; Siebers, Jeffrey V

2010-01-01

316

An introduction to Monte Carlo methods  

NASA Astrophysics Data System (ADS)

Monte Carlo simulations are methods for simulating statistical systems. The aim is to generate a representative ensemble of configurations to access thermodynamical quantities without the need to solve the system analytically or to perform an exact enumeration. The main principles of Monte Carlo simulations are ergodicity and detailed balance. The Ising model is a lattice spin system with nearest neighbor interactions that is appropriate to illustrate different examples of Monte Carlo simulations. It displays a second order phase transition between disordered (high temperature) and ordered (low temperature) phases, leading to different strategies of simulations. The Metropolis algorithm and the Glauber dynamics are efficient at high temperature. Close to the critical temperature, where the spins display long range correlations, cluster algorithms are more efficient. We introduce the rejection free (or continuous time) algorithm and describe in details an interesting alternative representation of the Ising model using graphs instead of spins with the so-called Worm algorithm. We conclude with an important discussion of the dynamical effects such as thermalization and correlation time.

Walter, J.-C.; Barkema, G. T.

2015-01-01

317

Chemical Potentials by Monte Carlo Simulations Model  

NSDL National Science Digital Library

The Chemical Potentials by Monte Carlo Simulations Model performs canonical (NVT) and isothermal-isobaric (NPT) Monte Carlo simulations focusing the calculation of chemical potentials, for the fluid phases of the Lennard-Jones system, by using the virtual particle insertion method of Widom. Although it can not determine phase-equilibrium directly, the gas-liquid line can be approached as illustrated in the included case study. The model paves the way to uVT and Gibbs Ensemble simulations, and shows the limitation of Widom's method at high fluid densities. The Chemical Potentials by Monte Carlo Simulations Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.

2013-10-30

318

A structured approach for the assessment of system availability and reliability using Monte Carlo simulation  

Microsoft Academic Search

Purpose – This paper proposes a method to model and assess the availability and reliability of a system when numerous factors such as system complexity, wide range of failure modes, environment, and sustainability may influence system behaviour. Design\\/methodology\\/approach – The approach for reliability\\/availability study is using continuous time stochastic simulation (Monte Carlo simulation) and is based on seven steps for

Adolfo Crespo Marquez; Benoît Iung

2007-01-01

319

Analyses of infectious disease data from household outbreaks by Markov chain Monte Carlo methods  

Microsoft Academic Search

This paper exploresthe use of Markov chain Monte Carlo (MCMC) methods for the analysis ofinfectious disease data, with the hope that they will permit analyses to be madeunder more realistic assumptions. Two important kinds of data sets are considered,containing temporal and non-temporal information respectively, from outbreaks ofmeasles and influenza. Stochastic epidemic models are used to describe the processesthat generate the

Philip D. ONeill; David J. Balding; Niels G. Becker; Mervi Eerola; Denis Mollison

2000-01-01

320

The Markov chain Monte Carlo method: an approach to approximate counting and integration  

Microsoft Academic Search

In the area of statistical physics, Monte Carlo algorithms based on Markov chain simulation have been in use for many years. The validity of these algorithms depends cru- cially on the rate of convergence to equilibrium of the Markov chain being simulated. Unfortunately, the classical theory of stochastic processes hardly touches on the sort of non-asymptotic analysis required in this

Mark Jerrum; Alistair Sinclair

1996-01-01

321

Guideline of Monte Carlo calculation. Neutron/gamma ray transport simulation by Monte Carlo method  

E-print Network

This report condenses basic theories and advanced applications of neutron/gamma ray transport calculations in many fields of nuclear energy research. Chapters 1 through 5 treat historical progress of Monte Carlo methods, general issues of variance reduction technique, cross section libraries used in continuous energy Monte Carlo codes. In chapter 6, the following issues are discussed: fusion benchmark experiments, design of ITER, experiment analyses of fast critical assembly, core analyses of JMTR, simulation of pulsed neutron experiment, core analyses of HTTR, duct streaming calculations, bulk shielding calculations, neutron/gamma ray transport calculations of the Hiroshima atomic bomb. Chapters 8 and 9 treat function enhancements of MCNP and MVP codes, and a parallel processing of Monte Carlo calculation, respectively. An important references are attached at the end of this report.

2002-01-01

322

THE MONTE CARLO ALGORITHM WITH A PSEUDO--RANDOM GENERATOR  

E-print Network

write the Monte Carlo algorithm with a pseudo­random generator. When the distribution ae is uniform, i.eTHE MONTE CARLO ALGORITHM WITH A PSEUDO--RANDOM GENERATOR J. F. Traub Department of Computer Carlo algorithm for the approximation of multivariate integrals when a pseudo­random generator is used

Traub, Joseph F.

323

State-of-the-art Monte Carlo 1988  

SciTech Connect

Particle transport calculations in highly dimensional and physically complex geometries, such as detector calibration, radiation shielding, space reactors, and oil-well logging, generally require Monte Carlo transport techniques. Monte Carlo particle transport can be performed on a variety of computers ranging from APOLLOs to VAXs. Some of the hardware and software developments, which now permit Monte Carlo methods to be routinely used, are reviewed in this paper. The development of inexpensive, large, fast computer memory, coupled with fast central processing units, permits Monte Carlo calculations to be performed on workstations, minicomputers, and supercomputers. The Monte Carlo renaissance is further aided by innovations in computer architecture and software development. Advances in vectorization and parallelization architecture have resulted in the development of new algorithms which have greatly reduced processing times. Finally, the renewed interest in Monte Carlo has spawned new variance reduction techniques which are being implemented in large computer codes. 45 refs.

Soran, P.D.

1988-06-28

324

Quantum Monte Carlo for vibrating molecules  

SciTech Connect

Quantum Monte Carlo (QMC) has successfully computed the total electronic energies of atoms and molecules. The main goal of this work is to use correlation function quantum Monte Carlo (CFQMC) to compute the vibrational state energies of molecules given a potential energy surface (PES). In CFQMC, an ensemble of random walkers simulate the diffusion and branching processes of the imaginary-time time dependent Schroedinger equation in order to evaluate the matrix elements. The program QMCVIB was written to perform multi-state VMC and CFQMC calculations and employed for several calculations of the H{sub 2}O and C{sub 3} vibrational states, using 7 PES`s, 3 trial wavefunction forms, two methods of non-linear basis function parameter optimization, and on both serial and parallel computers. In order to construct accurate trial wavefunctions different wavefunctions forms were required for H{sub 2}O and C{sub 3}. In order to construct accurate trial wavefunctions for C{sub 3}, the non-linear parameters were optimized with respect to the sum of the energies of several low-lying vibrational states. In order to stabilize the statistical error estimates for C{sub 3} the Monte Carlo data was collected into blocks. Accurate vibrational state energies were computed using both serial and parallel QMCVIB programs. Comparison of vibrational state energies computed from the three C{sub 3} PES`s suggested that a non-linear equilibrium geometry PES is the most accurate and that discrete potential representations may be used to conveniently determine vibrational state energies.

Brown, W.R. [Univ. of California, Berkeley, CA (United States). Chemistry Dept.]|[Lawrence Berkeley National Lab., CA (United States). Chemical Sciences Div.

1996-08-01

325

HS-WCA-LJ Monte Carlo Model  

NSDL National Science Digital Library

The HS-WCA-LJ Monte Carlo Model performs simultaneous canonical Monte Carlo (MC) simulations of 108, 256 or 500 particles interacting through the hard sphere (HS), the Weeks, Chandler and Andersen (WCA) and the Lennard-Jones (LJ) pair potentials. It was inspired by the review of Chandler, Weeks and Anderson on WCA theory, illustrating that "the attractive interactions help fix the volume of the system, but the arrangements and motions of molecules within that volume are determined primarily by the local packing and steric effects produced by the repulsive forces". The radial distribution functions for the three systems are plotted after every MC cycle, at densities and temperatures chosen by the user, and the data Tables display thermodynamic results from the LJ and WCA potentials. The thermodynamics of the HS system was addressed to another application cataloged at Open Source Physics. The objective of this application is: (i) to illustrate the canonical MC method with three different systems;(ii) to probe the densities and temperatures at which the HS and WCA potentials approach the structure(defined by the radial distribution functions) of the LJ system, regarding their use in perturbation theory. The HS-WCA-LJ Monte Carlo Model was developed using the Easy Java Simulations (EJS) modeling tool. It is distributed as a ready-to-run (compiled) Java archive. Double clicking the jar file will run the program if Java is installed. You can modify this simulation if you have EJS installed by right-clicking within the map and selecting "Open Ejs Model" from the pop-up menu item.

Fernandes, Fernando S.

2013-03-12

326

Monte Carlo study of disorder in HMTA  

NASA Astrophysics Data System (ADS)

We investigate disordered solids by automated fitting of a Monte Carlo simulation of a crystal to observed single-crystal diffuse X-ray scattering. This method has been extended to the study of crystals of relatively large organic molecules by using a z-matrix to describe the molecules. This allows exploration of motions within molecules. We refer to the correlated thermal motion observed in benzil, and to the occupational and thermal disorder in the 1:1 adduct of hexamethylenetetramine and azelaic acid, HMTA. The technique is capable of giving insight into modes of vibration within molecules and correlated motions between molecules.

Goossens, D. J.; Welberry, T. R.

2001-12-01

327

Introduction to the Diffusion Monte Carlo Method  

E-print Network

A self-contained and tutorial presentation of the diffusion Monte Carlo method for determining the ground state energy and wave function of quantum systems is provided. First, the theoretical basis of the method is derived and then a numerical algorithm is formulated. The algorithm is applied to determine the ground state of the harmonic oscillator, the Morse oscillator, the hydrogen atom, and the electronic ground state of the H2+ ion and of the H2 molecule. A computer program on which the sample calculations are based is available upon request.

Ioan Kosztin; Byron Faber; Klaus Schulten

1997-02-20

328

The Moment Guided Monte Carlo Method  

E-print Network

In this work we propose a new approach for the numerical simulation of kinetic equations through Monte Carlo schemes. We introduce a new technique which permits to reduce the variance of particle methods through a matching with a set of suitable macroscopic moment equations. In order to guarantee that the moment equations provide the correct solutions, they are coupled to the kinetic equation through a non equilibrium term. The basic idea, on which the method relies, consists in guiding the particle positions and velocities through moment equations so that the concurrent solution of the moment and kinetic models furnishes the same macroscopic quantities.

Pierre Degond; Giacomo Dimarco; Lorenzo Pareschi

2009-08-03

329

Monte Carlo Generation of Bohmian Trajectories  

E-print Network

We report on a Monte Carlo method that generates one-dimensional trajectories for Bohm's formulation of quantum mechanics that doesn't involve differentiation or integration of any equations of motion. At each time, t=n\\delta t (n=1,2,3,...), N particle positions are randomly sampled from the quantum probability density. Trajectories are built from the sorted N sampled positions at each time. These trajectories become the exact Bohm solutions in the limits N->\\infty and \\delta t -> 0. Higher dimensional problems can be solved by this method for separable wave functions. Several examples are given, including the two-slit experiment.

T. M. Coffey; R. E. Wyatt; W. C. Schieve

2008-07-01

330

Pollutants emission evaluation by Monte Carlo simulation  

SciTech Connect

This paper presents a method of Monte Carlo CO{sub 2} emission pollutants of generation system simulation that combines the use of an auxiliary variable with optimum stratified sampling. This design seeks to enhance the precision of CO{sub 2} emission pollutants in generation system estimation at a reduced computation time. The techniques included are optimum stratified sampling and proportional estimate. The optimum stratification rule aims to remove any judgmental input and to render the stratification process entirely mechanistic. The estimator, given by proportional statistics of the sample, can avoid identification of the regression model and thus save computation time. Hence, the effectiveness on precision improvement is demonstrated in this paper.

Huang, S.R.; Hwang, C.C.; Bor, S.S.; Lin, Y.W. [Feng Chia Univ., Taichung (Taiwan, Province of China). Dept. of Electrical Engineering

1995-12-31

331

Monte Carlo simulation for the transport beamline  

SciTech Connect

In the framework of the ELIMED project, Monte Carlo (MC) simulations are widely used to study the physical transport of charged particles generated by laser-target interactions and to preliminarily evaluate fluence and dose distributions. An energy selection system and the experimental setup for the TARANIS laser facility in Belfast (UK) have been already simulated with the GEANT4 (GEometry ANd Tracking) MC toolkit. Preliminary results are reported here. Future developments are planned to implement a MC based 3D treatment planning in order to optimize shots number and dose delivery.

Romano, F.; Cuttone, G.; Jia, S. B.; Varisano, A. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania (Italy)] [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania (Italy); Attili, A.; Marchetto, F.; Russo, G. [INFN, Sezione di Torino, Via P.Giuria, 1 10125 Torino (Italy)] [INFN, Sezione di Torino, Via P.Giuria, 1 10125 Torino (Italy); Cirrone, G. A. P.; Schillaci, F.; Scuderi, V. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Institute of Physics Czech Academy of Science, ELI-Beamlines project, Na Slovance 2, Prague (Czech Republic)] [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Institute of Physics Czech Academy of Science, ELI-Beamlines project, Na Slovance 2, Prague (Czech Republic); Carpinelli, M. [INFN Sezione di Cagliari, c/o Dipartimento di Fisica, Università di Cagliari, Cagliari (Italy)] [INFN Sezione di Cagliari, c/o Dipartimento di Fisica, Università di Cagliari, Cagliari (Italy); Tramontana, A. [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Università di Catania, Dipartimento di Fisica e Astronomia, Via S. Sofia 64, Catania (Italy)] [INFN, Laboratori Nazionali del Sud, Via Santa Sofia 62, Catania, Italy and Università di Catania, Dipartimento di Fisica e Astronomia, Via S. Sofia 64, Catania (Italy)

2013-07-26

332

Monte Carlo methods beyond detailed balance  

NASA Astrophysics Data System (ADS)

Monte Carlo algorithms are nearly always based on the concept of detailed balance and ergodicity. In this paper we focus on algorithms that do not satisfy detailed balance. We introduce a general method for designing non-detailed balance algorithms, starting from a conventional algorithm satisfying detailed balance. This approach is first applied to a very simple model, which shows the basic viability of the method. Then we apply it to the Ising model, where we find that the method is an improvement compared to the standard Metropolis algorithm, be it with a modest gain of a factor 2.3.

Schram, Raoul D.; Barkema, Gerard T.

2015-01-01

333

Archimedes, the Free Monte Carlo simulator  

E-print Network

Archimedes is the GNU package for Monte Carlo simulations of electron transport in semiconductor devices. The first release appeared in 2004 and since then it has been improved with many new features like quantum corrections, magnetic fields, new materials, GUI, etc. This document represents the first attempt to have a complete manual. Many of the Physics models implemented are described and a detailed description is presented to make the user able to write his/her own input deck. Please, feel free to contact the author if you want to contribute to the project.

Sellier, Jean Michel D

2012-01-01

334

Monte Carlo comparison of quasielectron wave functions  

SciTech Connect

Variational Monte Carlo calculations of the quasielectron and quasihole excitation energies in the fractional quantum Hall effect have been carried out at filling fractions {nu}=1/3, 1/5, and 1/7. For the quasielectron both the trial wave function originally proposed by Laughlin and the composite-fermion wave function proposed by Jain have been used. We find that for long-range Coulomb interactions the results obtained using these two wave functions are essentially the same, though the energy gap obtained using the composite-fermion quasielectron is slightly smaller, and closer to extrapolated exact-diagonalization results. thinsp {copyright} {ital 1998} {ital The American Physical Society}

Melik-Alaverdian, V.; Bonesteel, N.E. [National High Magnetic Field Laboratory and Department of Physics, Florida State University, Tallahassee, Florida 32306-4005 (United States)] [National High Magnetic Field Laboratory and Department of Physics, Florida State University, Tallahassee, Florida 32306-4005 (United States)

1998-07-01

335

Direct aperture optimization for IMRT using Monte Carlo generated beamlets  

Microsoft Academic Search

This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment

Alanah M. Bergman; Karl Bush; Marie-Pierre Milette; I. Antoniu Popescu; Karl Otto; Cheryl Duzenli

2006-01-01

336

Discrete diffusion Monte Carlo for frequency-dependent radiative transfer  

SciTech Connect

Discrete Diffusion Monte Carlo (DDMC) is a technique for increasing the efficiency of Implicit Monte Carlo radiative-transfer simulations. In this paper, we develop an extension of DDMC for frequency-dependent radiative transfer. We base our new DDMC method on a frequency-integrated diffusion equation for frequencies below a specified threshold. Above this threshold we employ standard Monte Carlo. With a frequency-dependent test problem, we confirm the increased efficiency of our new DDMC technique.

Densmore, Jeffrey D [Los Alamos National Laboratory; Kelly, Thompson G [Los Alamos National Laboratory; Urbatish, Todd J [Los Alamos National Laboratory

2010-11-17

337

Stochastic Approximation Approach to Stochastic Programming  

E-print Network

Monte Carlo sampling techniques, namely, the Stochastic Approximation (SA) and the Sample. Average ... than 5. The aim of this paper is to compare two computational approaches based on Monte. Carlo sampling ..... does not exceed M2.

2007-10-03

338

Adaptive monte carlo methods for rare event simulation: adaptive monte carlo methods for rare event simulations  

Microsoft Academic Search

We review two types of adaptive Monte Carlo methods for rare event simulations. These methods are based on importance sampling. The first approach selects importance sampling distributions by minimizing the variance of importance sampling estimator. The second approach selects importance sampling distributions by minimizing the cross entropy to the optimal importance sampling distribution. We also review the basic concepts of

Ming-hua Hsieh

2002-01-01

339

Reduced Density Matrices in Full Configuration Interaction Quantum Monte Carlo  

NASA Astrophysics Data System (ADS)

Reduced density matrices are a powerful construct in quantum chemistry, providing a compact representation of highly multi-determinantal wavefunctions, from which the expectation values of important physical properties can be extracted, including multipole moments, polarizabilities and nuclear forces^1,2. Full configuration interaction quantum Monte Carlo (FCIQMC)^3 and its initiator extension (i-FCIQMC)^4 perform a stochastic propagation of signed walkers within a space of Slater determinants to achieve FCI-quality energies without the need to store the complete wavefunction. We present here a method for a stochastic calculation of the 1- and 2-body reduced density matrices within the framework of (i)-FCIQMC, and apply this formulation to a range of archetypal molecular systems. Consideration is also given to the source and nature of systematic and stochastic error, and regimes to effectively alleviate these errors are discussed^5. ^1 P.-O. L"owdin, Phys. Rev. 97, 1474 (1955). ^2 C. A. Coulson, Rev. Mod. Phys. 32, 170 (1960). ^3 G. H. Booth, A. Thom, and A. Alavi, J. Chem. Phys. 131, 054106 (2009). ^4 D. Cleland, G. H. Booth, and A. Alavi, J. Chem. Phys. 132, 041103 (2010). ^5 D. Cleland, PhD thesis, University of Cambridge, 2012.

Overy, Catherine; Cleland, Deidre; Booth, George H.; Shepherd, James J.; Alavi, Ali

2013-03-01

340

Sequential Monte Carlo N d d F it & A d D tNando de Freitas & Arnaud Doucet  

E-print Network

Sequential Monte Carlo N d d F it & A d D tNando de Freitas & Arnaud Doucet University of British · Part I Arnaud ­ 50min ­ Monte Carlo Sequential Monte Carlo­ Sequential Monte Carlo ­ Theoretical ­ Monte Carlo Sequential Monte Carlo 20th century­ Sequential Monte Carlo ­ Theoretical convergence

de Freitas, Nando

341

Monte Carlo source convergence and the Whitesides problem  

SciTech Connect

The issue of fission source convergence in Monte Carlo eigenvalue calculations is of interest because of the potential consequences of erroneous criticality safety calculations. In this work, the authors compare two different techniques to improve the source convergence behavior of standard Monte Carlo calculations applied to challenging source convergence problems. The first method, super-history powering, attempts to avoid discarding important fission sites between generations by delaying stochastic sampling of the fission site bank until after several generations of multiplication. The second method, stratified sampling of the fission site bank, explicitly keeps the important sites even if conventional sampling would have eliminated them. The test problems are variants of Whitesides' Criticality of the World problem in which the fission site phase space was intentionally undersampled in order to induce marginally intolerable variability in local fission site populations. Three variants of the problem were studied, each with a different degree of coupling between fissionable pieces. Both the superhistory powering method and the stratified sampling method were shown to improve convergence behavior, although stratified sampling is more robust for the extreme case of no coupling. Neither algorithm completely eliminates the loss of the most important fissionable piece, and if coupling is absent, the lost piece cannot be recovered unless its sites from earlier generations have been retained. Finally, criteria for measuring source convergence reliability are proposed and applied to the test problems.

Blomquist, R. N.

2000-02-25

342

Estimating rock mass properties using Monte Carlo simulation: Ankara andesites  

NASA Astrophysics Data System (ADS)

In the paper, a previously introduced method ( Sari, 2009) is applied to the problem of estimating the rock mass properties of Ankara andesites. For this purpose, appropriate closed form (parametric) distributions are described for intact rock and discontinuity parameters of the Ankara andesites at three distinct weathering grades. Then, these distributions are included as inputs in the Rock Mass Rating ( RMR) classification system prepared in a spreadsheet model. A stochastic analysis is carried out to evaluate the influence of correlations between relevant distributions on the simulated RMR values. The model is also used in Monte Carlo simulations to estimate the possible ranges of the Hoek-Brown strength parameters of the rock under investigation. The proposed approach provides a straightforward and effective assessment of the variability of the rock mass properties. Hence, a wide array of mechanical characteristics can be adequately represented in any preliminary design consideration for a given rock mass.

Sari, Mehmet; Karpuz, Celal; Ayday, Can

2010-07-01

343

Reverse Monte Carlo modeling in confined systems  

SciTech Connect

An extension of the well established Reverse Monte Carlo (RMC) method for modeling systems under close confinement has been developed. The method overcomes limitations induced by close confinement in systems such as fluids adsorbed in microporous materials. As a test of the method, we investigate a model system of {sup 36}Ar adsorbed into two zeolites with significantly different pore sizes: Silicalite-I (a pure silica form of ZSM-5 zeolite, characterized by relatively narrow channels forming a 3D network) at partial and full loadings and siliceous Faujasite (which exhibits relatively wide channels and large cavities). The model systems are simulated using grand canonical Monte Carlo and, in each case, its structure factor is used as input for the proposed method, which shows a rapid convergence and yields an adsorbate microscopic structure in good agreement with that of the model system, even to the level of three body correlations, when these are induced by the confining media. The application to experimental systems is straightforward incorporating factors such as the experimental resolution and appropriate q-sampling, along the lines of previous experiences of RMC modeling of powder diffraction data including Bragg and diffuse scattering.

Sánchez-Gil, V.; Noya, E. G.; Lomba, E. [Instituto de Química Física Rocasolano, CSIC, Serrano 119, E-28006 Madrid (Spain)] [Instituto de Química Física Rocasolano, CSIC, Serrano 119, E-28006 Madrid (Spain)

2014-01-14

344

Monte Carlo radiative transfer in protoplanetary disks  

E-print Network

We present a new continuum 3D radiative transfer code, MCFOST, based on a Monte-Carlo method. MCFOST can be used to calculate (i) monochromatic images in scattered light and/or thermal emission, (ii) polarisation maps, (iii) interferometric visibilities, (iv) spectral energy distributions and (v) dust temperature distributions of protoplanetary disks. Several improvements to the standard Monte Carlo method are implemented in MCFOST to increase efficiency and reduce convergence time, including wavelength distribution adjustments, mean intensity calculations and an adaptive sampling of the radiation field. The reliability and efficiency of the code are tested against a previously defined benchmark, using a 2D disk configuration. No significant difference (no more than 10%, and generally much less) is found between the temperatures and SEDs calculated by MCFOST and by other codes included in the benchmark. A study of the lowest disk mass detectable by Spitzer, around young stars, is presented and the colours of ``representative'' parametric disks are compared to recent IRAC and MIPS Spitzer colours of solar-like young stars located in nearby star forming regions.

Christophe Pinte; Francois Menard; Gaspard Duchene; Pierre Bastien

2006-06-22

345

Simple Monte Carlo model for crowd dynamics  

NASA Astrophysics Data System (ADS)

In this paper, we introduce a simple Monte Carlo method for simulating the dynamics of a crowd. Within our model a collection of hard-disk agents is subjected to a series of two-stage steps, implying (i) the displacement of one specific agent followed by (ii) a rearrangement of the rest of the group through a Monte Carlo dynamics. The rules for the combined steps are determined by the specific setting of the granular flow, so that our scheme should be easily adapted to describe crowd dynamics issues of many sorts, from stampedes in panic scenarios to organized flow around obstacles or through bottlenecks. We validate our scheme by computing the serving times statistics of a group of agents crowding to be served around a desk. In the case of a size homogeneous crowd, we recover intuitive results prompted by physical sense. However, as a further illustration of our theoretical framework, we show that heterogeneous systems display a less obvious behavior, as smaller agents feature shorter serving times. Finally, we analyze our results in the light of known properties of nonequilibrium hard-disk fluids and discuss general implications of our model.

Piazza, Francesco

2010-08-01

346

Reverse Monte Carlo modeling in confined systems  

NASA Astrophysics Data System (ADS)

An extension of the well established Reverse Monte Carlo (RMC) method for modeling systems under close confinement has been developed. The method overcomes limitations induced by close confinement in systems such as fluids adsorbed in microporous materials. As a test of the method, we investigate a model system of 36Ar adsorbed into two zeolites with significantly different pore sizes: Silicalite-I (a pure silica form of ZSM-5 zeolite, characterized by relatively narrow channels forming a 3D network) at partial and full loadings and siliceous Faujasite (which exhibits relatively wide channels and large cavities). The model systems are simulated using grand canonical Monte Carlo and, in each case, its structure factor is used as input for the proposed method, which shows a rapid convergence and yields an adsorbate microscopic structure in good agreement with that of the model system, even to the level of three body correlations, when these are induced by the confining media. The application to experimental systems is straightforward incorporating factors such as the experimental resolution and appropriate q-sampling, along the lines of previous experiences of RMC modeling of powder diffraction data including Bragg and diffuse scattering.

Sánchez-Gil, V.; Noya, E. G.; Lomba, E.

2014-01-01

347

Quantum Monte Carlo methods for nuclear physics  

E-print Network

Quantum Monte Carlo methods have proved very valuable to study the structure and reactions of light nuclei and nucleonic matter starting from realistic nuclear interactions and currents. These ab-initio calculations reproduce many low-lying states and transition moments in light nuclei, and simultaneously predict many properties of light nuclei and neutron matter over a rather wide range of energy and momenta. We review the nuclear interactions and currents, and describe the continuum Quantum Monte Carlo methods used in nuclear physics. These methods are similar to those used in condensed matter and electronic structure but naturally include spin-isospin, tensor, spin-orbit, and three-body interactions. We present a variety of results including the low-lying spectra of light nuclei, nuclear form factors, and transition matrix elements. We also describe low-energy scattering techniques, studies of the electroweak response of nuclei relevant in electron and neutrino scattering, and the properties of dense nucleonic matter as found in neutron stars. A coherent picture of nuclear structure and dynamics emerges based upon rather simple but realistic interactions and currents.

J. Carlson; S. Gandolfi; F. Pederiva; Steven C. Pieper; R. Schiavilla; K. E. Schmidt; R. B. Wiringa

2014-12-09

348

A Monte Carlo Simulation of Space Radiation  

NASA Astrophysics Data System (ADS)

A state-of-the-art Monte-Carlo computer simulation of the space radiation environment using particle transport codes from CERN and INFN (Italy) is described. Spacecraft subject to space radiation are visualized much like a detector in an accelerator beamline. Standard software techniques simulate the evolution of particle cascades through an accurate isotopic-compositional model of the vehicle. The simulation uses the latest known results in low-energy and high-energy physics derived from an architecture called AliRoot structured about a Virtual Monte Carlo whose transport engines are FLUKA and GEANT4 [1]. The output is a detailed depiction of the total space radiation environment, including the secondary albedoes produced. The neutron albedo is of particular concern. Beyond doing the physics transport of incident flux using FLUKA, the simulation provides a self-contained stand-alone object-oriented analysis and visualization infrastructure. The latter is known as ROOT, recently adopted for CDF at Fermilab. Complex spacecraft geometries are represented by aerospace finite element models (FEMs) which readily lend themselves to CAD (Computer-Aided Design) analysis. [1] Brun, R., Carminati, F., & Rademakers, F., in Proc. Int'l. Conf. Computing High-Energy and Nuclear Physics, CHEP (2000).

Pinsky, Lawrence; MacGibbon, Jane; Wilson, Thomas

2000-10-01

349

Multilevel Monte Carlo simulation of Coulomb collisions  

NASA Astrophysics Data System (ADS)

We present a new, for plasma physics, highly efficient multilevel Monte Carlo numerical method for simulating Coulomb collisions. The method separates and optimally minimizes the finite-timestep and finite-sampling errors inherent in the Langevin representation of the Landau-Fokker-Planck equation. It does so by combining multiple solutions to the underlying equations with varying numbers of timesteps. For a desired level of accuracy ?, the computational cost of the method is O(?-2) or O(?-2(), depending on the underlying discretization, Milstein or Euler-Maruyama respectively. This is to be contrasted with a cost of O(?-3) for direct simulation Monte Carlo or binary collision methods. We successfully demonstrate the method with a classic beam diffusion test case in 2D, making use of the Lévy area approximation for the correlated Milstein cross terms, and generating a computational saving of a factor of 100 for ?=10-5. We discuss the importance of the method for problems in which collisions constitute the computational rate limiting step, and its limitations.

Rosin, M. S.; Ricketson, L. F.; Dimits, A. M.; Caflisch, R. E.; Cohen, B. I.

2014-10-01

350

Constrained-Path Quantum Monte Carlo Approach for the Nuclear Shell Model  

NASA Astrophysics Data System (ADS)

A new quantum Monte Carlo approach is proposed to investigate low-lying states of nuclei within the shell model. The formalism relies on a variational symmetry-restored wave function to guide the underlying Brownian motion. Sign or phase problems that usually plague quantum Monte Carlo fermionic simulations are controlled by constraining stochastic paths through a fixed-node-like approximation. Exploratory results in the sd and pf valence spaces with realistic effective interactions are presented. They prove the ability of the scheme to yield nearly exact yrast spectroscopies for both even- and odd-mass nuclei.

Bonnard, J.; Juillet, O.

2013-07-01

351

Population Monte Carlo algorithms Yukito Iba The Institute of Statistical Mathematics  

E-print Network

279 ¤ Population Monte Carlo algorithms Yukito Iba The Institute of Statistical Mathematics iba@ism.ac.jp, http://www.ism.ac.jp/~iba/ keywords: quantum Monte Carlo, transfer-matrix Monte Carlo, Monte Carlo filter, sequential Monte Carlo, pruned-enriched Rosenbluth method, annealed importance sampling, genetic

Iba, Yukito

352

Case Study: Monte Carlo Simulation Monte Carlo simulation uses random numbers and probability to solve problems. This method has a wide range of  

E-print Network

Case Study: Monte Carlo Simulation Monte Carlo simulation uses random numbers and probability, chemistry, and finance. This section gives an example of using Monte Carlo simulation for estimating . To estimate using the Monte Carlo method, draw a circle with its bounding square as shown below. x y 1-1 1 -1

Liang, Y. Daniel

353

Economic Risk Analysis: Using Analytical and Monte Carlo Techniques.  

ERIC Educational Resources Information Center

Describes the development and instructional use of a Microsoft Excel spreadsheet template that facilitates analytical and Monte Carlo risk analysis of investment decisions. Discusses a variety of risk assessment methods followed by applications of the analytical and Monte Carlo methods. Uses a case study to illustrate use of the spreadsheet tool…

O'Donnell, Brendan R.; Hickner, Michael A.; Barna, Bruce A.

2002-01-01

354

Monte Carlo Test Assembly for Item Pool Analysis and Extension  

ERIC Educational Resources Information Center

A new test assembly algorithm based on a Monte Carlo random search is presented in this article. A major advantage of the Monte Carlo test assembly over other approaches (integer programming or enumerative heuristics) is that it performs a uniform sampling from the item pool, which provides every feasible item combination (test) with an equal…

Belov, Dmitry I.; Armstrong, Ronald D.

2005-01-01

355

A Primer in Monte Carlo Integration Using Mathcad  

ERIC Educational Resources Information Center

The essentials of Monte Carlo integration are presented for use in an upper-level physical chemistry setting. A Mathcad document that aids in the dissemination and utilization of this information is described and is available in the Supporting Information. A brief outline of Monte Carlo integration is given, along with ideas and pedagogy for…

Hoyer, Chad E.; Kegerreis, Jeb S.

2013-01-01

356

Kinetic Monte Carlo Simulations of dislocations in heteroepitaxial growth  

E-print Network

Kinetic Monte Carlo Simulations of dislocations in heteroepitaxial growth F. Much #3; , M. Ahr, M the lattice constants of the substrate and the adsorbate from Kinetic Monte Carlo (KMC) simulations of various phenomena observed in heteroepitaxial growth (see e.g. [1, 2]) including critical layer thickness

Biehl, Michael

357

Bayesian Calibration for Monte Carlo Localization Armita Kaboli  

E-print Network

Bayesian Calibration for Monte Carlo Localization Armita Kaboli and Michael Bowling and Petr 2004), have proven effective. In situations requiring a multimodal representation, Monte Carlo-prone. It becomes a more serious prob- lem when one considers that a robot's physical properties (e.g., tire

Bowling, Michael

358

Parallel computations of eigenvalues based on a Monte Carlo approach  

E-print Network

Parallel computations of eigenvalues based on a Monte Carlo approach Ivan Dimov and Aneta.,bl. 25 A, Sofia, 1113, Bulgaria E­mail: anet@amigo.acad.bg, dimov@amigo.acad.bg Web site: http://www.acad.bg/BulRTD/math/dimov2.html Key words: Monte Carlo algorithms, eigenvalue, efficiency estimator, Markov chain, parallel

Dimov, Ivan

359

Efficient Energy Computation for Monte Carlo Simulation of Proteins  

E-print Network

Efficient Energy Computation for Monte Carlo Simulation of Proteins Itay Lotan1 and Fabian Schwarzer1 and Jean-Claude Latombe1 Dept. of Computer Science, Stanford University, Stanford, CA 94305 E-mail: [itayl, schwarzf, latombe]@cs.stanford.edu Abstract. Monte Carlo simulation (MCS) is a common methodology

Pratt, Vaughan

360

Monte Carlo Tree Search with Bayesian Model Averaging for the  

E-print Network

Monte Carlo Tree Search with Bayesian Model Averaging for the Game of Go John Jeong You A subthesis;#12;Abstract Computer Go is the next grand challenge for AI games research and in recent years, Monte Carlo, there has been little effort to learn from local simi- larities between search tree nodes (i.e., Go boards

Sanner, Scott

361

Monte Carlo Method for Multiple Knapsack Stefka Fidanova  

E-print Network

Monte Carlo Method for Multiple Knapsack Problem Stefka Fidanova CLPP { BAS, Acad. G. Bonchev str. bl.25A, 1113 So#12;a, Bulgaria fidanova@parallel.bas.bg Abstract. This paper describes Monte Carlo) procedure which can be coupled with the ACO algorithm to improve the eÃ?ciency of the solving of the MKP

Fidanova, Stefka

362

SIMPLE MONTE CARLO AND THE METROPOLIS ALGORITHM PETER MATH  

E-print Network

SIMPLE MONTE CARLO AND THE METROPOLIS ALGORITHM PETER MATH â?? E AND ERICH NOVAK Dedicated to our in the variability, and the simple Monte Carlo method provides an almost optimal algorithm. Under addi­ tional terms, these functions are given by an oracle, i.e., we assume that we can compute function values of f

Novak, Erich

363

Effect of statistical uncertainties on Monte Carlo treatment planning  

Microsoft Academic Search

This paper reviews the effect of statistical uncertainties on radiotherapy treatment planning using Monte Carlo simulations. We discuss issues related to the statistical analysis of Monte Carlo dose calculations for realistic clinical beams using various variance reduction or time saving techniques. We discuss the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and intensity-modulated

C.-M. Ma; J. S. Li; S. B. Jiang; T. Pawlicki; W. Xiong; L. H. Qin; J. Yang

2005-01-01

364

Parallel canonical Monte Carlo simulations through sequential updating of particles  

Microsoft Academic Search

In canonical Monte Carlo simulations, sequential updating of particles is equivalent to random updating due to particle indistinguishability. In contrast, in grand canonical Monte Carlo simulations, sequential implementation of the particle transfer steps in a dense grid of distinct points in space improves both the serial and the parallel efficiency of the simulation. The main advantage of sequential updating in

C. J. O'Keeffe; G. Orkoulas

2009-01-01

365

Monte Carlo query processing of uncertain multidimensional array data  

Microsoft Academic Search

Array database systems are architected for scientific and engineering applications. In these applications, the value of a cell is often imprecise and uncertain. There are at least two reasons that a Monte Carlo query processing algorithm is usually required for such uncertain data. Firstly, a probabilistic graphical model must often be used to model correlation, which requires a Monte Carlo

Tingjian Ge; David Grabiner

2011-01-01

366

Monte Carlo simulation and dosimetric verification of radiotherapy beam modifiers  

Microsoft Academic Search

Monte Carlo simulation of beam modifiers such as physical wedges and compensating filters has been performed with a rectilinear voxel geometry module. A modified version of the EGS4\\/DOSXYZ code has been developed for this purpose. The new implementations have been validated against the BEAM Monte Carlo code using its standard component modules (CMs) in several geometrical conditions. No significant disagreements

E. Spezi; D. G. Lewis; C. W. Smith

2001-01-01

367

Monte Carlo variance reduction approaches for non-Boltzmann tallies  

Microsoft Academic Search

Quantities that depend on the collective effects of groups of particles cannot be obtained from the standard Boltzmann transport equation. Monte Carlo estimates of these quantities are called non-Boltzmann tallies and have become increasingly important recently. Standard Monte Carlo variance reduction techniques were designed for tallies based on individual particles rather than groups of particles. Experience with non-Boltzmann tallies and

1992-01-01

368

Monte Carlo simulation for solving Fredholm integral equations  

Microsoft Academic Search

Purpose – The purpose of this paper is to provide a Monte Carlo variance reduction method based on Control variates to solve Fredholm integral equations of the second kind. Design\\/methodology\\/approach – A numerical algorithm consisted of the combined use of the successive substitution method and Monte Carlo simulation is established for the solution of Fredholm integral equations of the second

Rahman Farnoosh; Ebrahimi Morteza

2009-01-01

369

Monte Carlo radiation transport: A revolution in science  

Microsoft Academic Search

When Enrico Fermi, Stan Ulam, Nicholas Metropolis, John von Neuman, and Robert Richtmyer invented the Monte Carlo method fifty years ago, little could they imagine the far-flung consequences, the international applications, and the revolution in science epitomized by their abstract mathematical method. The Monte Carlo method is used in a wide variety of fields to solve exact computational models approximately

Hendricks

1993-01-01

370

Radiative heat transfer with quasi-monte carlo methods  

Microsoft Academic Search

Monte Carlo simulation is often used to solve radiative transfer problems wherecomplex physical phenomena and geometries must be handled. Slow convergenceis a well known disadvantage of such methods. In this paper we demonstratethat a significant improvement in computation time can be achieved by usingQuasi-Monte Carlo methods to simulate Rapid Thermal Processing, which is animportant technique for the production of semiconductor

A. Kersch; W. Morokoff; A. Schuster

1994-01-01

371

Quantum Monte Carlo Method for Attractive Coulomb Potentials  

E-print Network

Starting from an exact lower bound on the imaginary-time propagator, we present a Path-Integral Quantum Monte Carlo method that can handle singular attractive potentials. We illustrate the basic ideas of this Quantum Monte Carlo algorithm by simulating the ground state of hydrogen and helium.

J. S. Kole; H. De Raedt

2001-02-06

372

Sequential Monte Carlo Methods to Train Neural Network Models  

Microsoft Academic Search

We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent\\/sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to

João F. G. De Freitas; Mahesan Niranjan; Andrew H. Gee; Arnaud Doucet

2000-01-01

373

New sequential Monte Carlo methods for nonlinear dynamic systems  

Microsoft Academic Search

In this paper we present several new sequential Monte Carlo (SMC) algorithms for online estimation (filtering) of nonlinear dynamic systems. SMC has been shown to be a powerful tool for dealing with complex dynamic systems. It sequentially generates Monte Carlo samples from a proposal distribution, adjusted by a set of importance weight with respect to a target distribution, to facilitate

Dong Guo; Xiaodong Wang; Rong Chen

2005-01-01

374

Quasi-Monte Carlo Methods in Numerical Finance  

Microsoft Academic Search

This paper introduces and illustrates a new version of the Monte Carlo method that has attractive properties for the numerical valuation of derivatives. The traditional Monte Carlo method has proven to be a powerful and flexible tool for many types of derivatives calculations. Under the conventional approach pseudo-random numbers are used to evaluate the expression of interest. Unfortunately, the use

Corwin Joy; Phelim P. Boyle; Ken Seng Tan

1996-01-01

375

Recent Advances in Randomized Quasi-Monte Carlo Methods  

Microsoft Academic Search

We survey some of the recent developments on quasi-Monte Carlo (QMC) methods, which, in their basic form, are a deterministic counterpart to the Monte Carlo (MC) method. Our main focus is the applicability of these methods to practical problems that involve the estimation of a high-dimensional integral. We review several QMC constructions and different randomizations that have been proposed to

Pierre L’Ecuyer; Christiane Lemieux

376

Monte Carlo methods in an introductory electromagnetic course  

Microsoft Academic Search

Although the pedagogical value of introducing numerical methods such as finite-element methods, finite-difference methods, and moment methods in an introductory electromagnetics (EM) course has been recognized, no similar attempt has been made to introduce Monte Carlo methods. An attempt is made to fill this gap by presenting Monte Carlo procedures in simple terms that can be presented in an introductory

M. N. O. Sadiku

1990-01-01

377

Bayesian Inference in Econometric Models Using Monte Carlo Integration  

Microsoft Academic Search

Methods for the systematic application of Monte Carlo integration with importance sampling to Bayesian inference are developed. Conditions under which the numerical approximation converges almost surely to the true value with the number of Monte Carlo replications, and its numerical accuracy may be assessed reliably, are given. Importance sampling densities are derived from multivariate normal or student approximations to the

John Geweke

1989-01-01

378

Inverse Monte Carlo: a unified reconstruction algorithm for SPECT  

Microsoft Academic Search

Inverse Monte Carlo (IMOC) is presented as a unified reconstruction algorithm for Emission Computed Tomography (ECT) providing simultaneous compensation for scatter, attenuation, and the variation of collimator resolution with depth. The technique of inverse Monte Carlo is used to find an inverse solution to the photon transport equation (an integral equation for photon flux from a specified source) for a

Carey E. Floyd; R. E. Coleman; R. J. Jaszczak

1985-01-01

379

Implementation of stratified sampling for Monte Carlo applications  

SciTech Connect

Stratified sampling is a method used in Monte Carlo calculations to take advantage of certain known aspects of probability distributions. The sampling region is subdivided into discrete subregions, and each of these is sampled a preassigned number of times. A form of stratified sampling has been implemented into a major Monte Carlo particle transport computer code with very encouraging results.

Brown, R.S. Jr.; Hendricks, J.S.

1987-11-01

380

Quasi-Monte Carlo Sampling by Art B. Owen  

E-print Network

cube [0, 1)d. Next we consider how stratification methods, such as jittered sampling, can improve on stratified sampling. These stratification meth- ods balance the sampling points with respect to a largeChapter 1 Quasi-Monte Carlo Sampling by Art B. Owen In Monte Carlo (MC) sampling the sample

Owen, Art

381

Finite Time Analysis of Stratified Sampling for Monte Carlo  

E-print Network

Finite Time Analysis of Stratified Sampling for Monte Carlo Alexandra Carpentier INRIA Lille - Nord We consider the problem of stratified sampling for Monte-Carlo integration. We model this problem the final estimation error. This example is just one of many for which an efficient method of sampling

Boyer, Edmond

382

Optimally combining sampling techniques for Monte Carlo rendering  

Microsoft Academic Search

Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods. In these cases variance can often be signifi- cantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Nor- mally this

Eric Veach; Leonidas J. Guibas

1995-01-01

383

A MONTE CARLO SEQUENTIAL ESTIMATION OF POINT PROCESS OPTIMUM FILTERING FOR BRAIN MACHINE INTERFACES  

E-print Network

1 A MONTE CARLO SEQUENTIAL ESTIMATION OF POINT PROCESS OPTIMUM FILTERING FOR BRAIN MACHINE Monte Carlo Sequential Estimation for Point Processes.................................................29 Simulation of Monte Carlo Sequential Estimation on Neural Spike Train Decoding............32 Interpretation

Slatton, Clint

384

Monte Carlo technique in modeling ground motion coherence in sedimentary filled valleys  

E-print Network

Monte Carlo technique in modeling ground motion coherence in sedimentary filled valleys Arrigo propagation Monte Carlo numerical simulations Site effects a b s t r a c t Using a Monte Carlo method based

Cerveny, Vlastislav

385

SEQUENTIAL MONTE CARLO SAMPLERS FOR MARGINAL LIKELIHOOD COMPUTATION IN MULTIPLICATIVE EXPONENTIAL NOISE MODELS  

E-print Network

SEQUENTIAL MONTE CARLO SAMPLERS FOR MARGINAL LIKELIHOOD COMPUTATION IN MULTIPLICATIVE EXPONENTIAL Monte Carlo (SMC) samplers framework. We explore the performances of these estimators on two problems. Index Terms-- sequential Monte Carlo samplers, Itakura-Saito divergence, Nonnegative Matrix

Cemgil, A. Taylan

386

Crossing the mesoscale no-mans land via parallel kinetic Monte Carlo.  

SciTech Connect

The kinetic Monte Carlo method and its variants are powerful tools for modeling materials at the mesoscale, meaning at length and time scales in between the atomic and continuum. We have completed a 3 year LDRD project with the goal of developing a parallel kinetic Monte Carlo capability and applying it to materials modeling problems of interest to Sandia. In this report we give an overview of the methods and algorithms developed, and describe our new open-source code called SPPARKS, for Stochastic Parallel PARticle Kinetic Simulator. We also highlight the development of several Monte Carlo models in SPPARKS for specific materials modeling applications, including grain growth, bubble formation, diffusion in nanoporous materials, defect formation in erbium hydrides, and surface growth and evolution.

Garcia Cardona, Cristina (San Diego State University); Webb, Edmund Blackburn, III; Wagner, Gregory John; Tikare, Veena; Holm, Elizabeth Ann; Plimpton, Steven James; Thompson, Aidan Patrick; Slepoy, Alexander (U. S. Department of Energy, NNSA); Zhou, Xiao Wang; Battaile, Corbett Chandler; Chandross, Michael Evan

2009-10-01

387

Correlations in the Monte Carlo Glauber model  

E-print Network

Event-by-event fluctuations of observables are often modeled using the Monte Carlo Glauber model, in which the energy is initially deposited in sources associated with wounded nucleons. In this paper, we analyze in detail the correlations between these sources in proton-nucleus and nucleus-nucleus collisions. There are correlations arising from nucleon-nucleon correlations within each nucleus, and correlations due to the collision mechanism, which we dub twin correlations. We investigate this new phenomenon in detail. At the RHIC and LHC energies, correlations are found to have modest effects on size and eccentricity fluctuations, such that the Glauber model produces to a good approximation a collection of independent sources.

Jean-Paul Blaizot; Wojciech Broniowski; Jean-Yves Ollitrault

2014-09-12

388

Monte Carlo simulation of neutron scattering instruments  

SciTech Connect

A code package consisting of the Monte Carlo Library MCLIB, the executing code MC{_}RUN, the web application MC{_}Web, and various ancillary codes is proposed as an open standard for simulation of neutron scattering instruments. The architecture of the package includes structures to define surfaces, regions, and optical elements contained in regions. A particle is defined by its vector position and velocity, its time of flight, its mass and charge, and a polarization vector. The MC{_}RUN code handles neutron transport and bookkeeping, while the action on the neutron within any region is computed using algorithms that may be deterministic, probabilistic, or a combination. Complete versatility is possible because the existing library may be supplemented by any procedures a user is able to code. Some examples are shown.

Seeger, P.A.; Daemen, L.L.; Hjelm, R.P. Jr.

1998-12-01

389

MORSE Monte Carlo radiation transport code system  

SciTech Connect

This report is an addendum to the MORSE report, ORNL-4972, originally published in 1975. This addendum contains descriptions of several modifications to the MORSE Monte Carlo Code, replacement pages containing corrections, Part II of the report which was previously unpublished, and a new Table of Contents. The modifications include a Klein Nishina estimator for gamma rays. Use of such an estimator required changing the cross section routines to process pair production and Compton scattering cross sections directly from ENDF tapes and writing a new version of subroutine RELCOL. Another modification is the use of free form input for the SAMBO analysis data. This required changing subroutines SCORIN and adding new subroutine RFRE. References are updated, and errors in the original report have been corrected. (WHK)

Emmett, M.B.

1983-02-01

390

Quantum ice: a quantum Monte Carlo study.  

PubMed

Ice states, in which frustrated interactions lead to a macroscopic ground-state degeneracy, occur in water ice, in problems of frustrated charge order on the pyrochlore lattice, and in the family of rare-earth magnets collectively known as spin ice. Of particular interest at the moment are "quantum spin-ice" materials, where large quantum fluctuations may permit tunnelling between a macroscopic number of different classical ground states. Here we use zero-temperature quantum Monte Carlo simulations to show how such tunnelling can lift the degeneracy of a spin or charge ice, stabilizing a unique "quantum-ice" ground state-a quantum liquid with excitations described by the Maxwell action of (3+1)-dimensional quantum electrodynamics. We further identify a competing ordered squiggle state, and show how both squiggle and quantum-ice states might be distinguished in neutron scattering experiments on a spin-ice material. PMID:22401117

Shannon, Nic; Sikora, Olga; Pollmann, Frank; Penc, Karlo; Fulde, Peter

2012-02-10

391

Methods for Monte Carlo simulations of biomacromolecules  

PubMed Central

The state-of-the-art for Monte Carlo (MC) simulations of biomacromolecules is reviewed. Available methodologies for sampling conformational equilibria and associations of biomacromolecules in the canonical ensemble, given a continuum description of the solvent environment, are reviewed. Detailed sections are provided dealing with the choice of degrees of freedom, the efficiencies of MC algorithms and algorithmic peculiarities, as well as the optimization of simple movesets. The issue of introducing correlations into elementary MC moves, and the applicability of such methods to simulations of biomacromolecules is discussed. A brief discussion of multicanonical methods and an overview of recent simulation work highlighting the potential of MC methods are also provided. It is argued that MC simulations, while underutilized biomacromolecular simulation community, hold promise for simulations of complex systems and phenomena that span multiple length scales, especially when used in conjunction with implicit solvation models or other coarse graining strategies. PMID:20428473

Vitalis, Andreas; Pappu, Rohit V.

2010-01-01

392

Exploring Theory Space with Monte Carlo Reweighting  

E-print Network

Theories of new physics often involve a large number of unknown parameters which need to be scanned. Additionally, a putative signal in a particular channel may be due to a variety of distinct models of new physics. This makes experimental attempts to constrain the parameter space of motivated new physics models with a high degree of generality quite challenging. We describe how the reweighting of events may allow this challenge to be met, as fully simulated Monte Carlo samples generated for arbitrary benchmark models can be effectively re-used. In particular, we suggest procedures that allow more efficient collaboration between theorists and experimentalists in exploring large theory parameter spaces in a rigorous way at the LHC.

James S. Gainer; Joseph Lykken; Konstantin T. Matchev; Stephen Mrenna; Myeonghun Park

2014-12-25

393

Monte Carlo stratified source-sampling  

SciTech Connect

In 1995, at a conference on criticality safety, a special session was devoted to the Monte Carlo {open_quotes}eigenvalue of the world{close_quotes} problem. Argonne presented a paper, at that session, in which the anomalies originally observed in that problem were reproduced in a much simplified model-problem configuration, and removed by a version of stratified source-sampling. The original test-problem was treated by a special code designed specifically for that purpose. Recently ANL started work on a method for dealing with more realistic eigenvalue of the world configurations, and has been incorporating this method into VIM. The original method has been modified to take into account real-world statistical noise sources not included in the model problem. This paper constitutes a status report on work still in progress.

Blomquist, R.N.; Gelbard, E.M.

1997-09-01

394

Monte Carlo approaches to effective field theories  

SciTech Connect

In this paper, we explore the application of continuum Monte Carlo methods to effective field theory models. Effective field theories, in this context, are those in which a Fock space decomposition of the state is useful. These problems arise both in nuclear and condensed matter physica. In nuclear physics, much work has been done on effective field theories of mesons and baryons. While the theories are not fundamental, they should be able to describe nuclear properties at low energy and momentum scales. After describing the methods, we solve two simple scalar field theory problems; the polaron and two nucleons interacting through scalar meson exchange. The methods presented here are rather straightforward extensions of methods used to solve quantum mechanics problems. Monte Carlo methods are used to avoid the truncation inherent in a Tamm-Dancoff approach and its associated difficulties. Nevertheless, the methods will be most valuable when the Fock space decomposition of the states is useful. Hence, while they are not intended for ab initio studies of QCD, they may prove valuable in studies of light nuclei, or for systems of interacting electrons and phonons. In these problems a Fock space decomposition can be used to reduce the number of degrees of freedom and to retain the rotational symmetries exactly. The problems we address here are comparatively simple, but offer useful initial tests of the method. We present results for the polaron and two non-relativistic nucleons interacting through scalar meson exchange. In each case, it is possible to integrate out the boson degrees of freedom exactly, and obtain a retarded form of the action that depends only upon the fermion paths. Here we keep the explicit bosons, though, since we would like to retain information about the boson components of the states and it will be necessary to keep these components in order to treat non-scalar of interacting bosonic fields.

Carlson, J. (Los Alamos National Lab., NM (United States)); Schmidt, K.E. (Arizona State Univ., Tempe, AZ (United States). Dept. of Physics)

1991-01-01

395

Monte Carlo simulations of peptide adsorption on solid surfaces (Monte Carlo simulations of peptide adsorption)  

Microsoft Academic Search

Monte Carlo simulations (MC) were used to study the adsorption of a negatively charged peptide (ASP-ASP-ILE-ILE-ASP-ASP-ILE-ILE) dissolved in water onto charged surfaces and in vacuum onto neutral surfaces. When the peptide was placed between two charged surfaces, it always adsorbed sideways onto the positively charged surface even when it was initially positioned at the negatively charged one. The structure of

Da Song; Daniel Forciniti

2001-01-01

396

Reactive Monte Carlo and grand-canonical Monte Carlo simulations of the propene metathesis reaction system  

Microsoft Academic Search

The influence of silicalite-1 pores on the reaction equilibria and the selectivity of the propene metathesis reaction system in the temperature range between 300 and 600 K and the pressure range from 0.5 to 7 bars has been investigated with molecular simulations. The reactive Monte Carlo (RxMC) technique was applied for bulk-phase simulations in the isobaric-isothermal ensemble and for two

Niels Hansen; Sven Jakobtorweihen; Frerich J. Keil

2005-01-01

397

Why Quasi-Monte Carlo is Better Than Monte Carlo or Latin Hypercube Sampling for Statistical Circuit Analysis  

Microsoft Academic Search

At the nanoscale, no circuit parameters are truly deterministic; most quantities of practical interest present themselves as probability distributions. Thus, Monte Carlo techniques comprise the strategy of choice for statistical circuit analysis. There are many challenges in applying these techniques efficiently: circuit size, nonlinearity, simulation time, and required accuracy often conspire to make Monte Carlo analysis expensive and slow. Are

Amith Singhee; Rob A. Rutenbar

2010-01-01

398

A hybrid Monte Carlo and response matrix Monte Carlo method in criticality calculation  

SciTech Connect

Full core calculations are very useful and important in reactor physics analysis, especially in computing the full core power distributions, optimizing the refueling strategies and analyzing the depletion of fuels. To reduce the computing time and accelerate the convergence, a method named Response Matrix Monte Carlo (RMMC) method based on analog Monte Carlo simulation was used to calculate the fixed source neutron transport problems in repeated structures. To make more accurate calculations, we put forward the RMMC method based on non-analog Monte Carlo simulation and investigate the way to use RMMC method in criticality calculations. Then a new hybrid RMMC and MC (RMMC+MC) method is put forward to solve the criticality problems with combined repeated and flexible geometries. This new RMMC+MC method, having the advantages of both MC method and RMMC method, can not only increase the efficiency of calculations, also simulate more complex geometries rather than repeated structures. Several 1-D numerical problems are constructed to test the new RMMC and RMMC+MC method. The results show that RMMC method and RMMC+MC method can efficiently reduce the computing time and variations in the calculations. Finally, the future research directions are mentioned and discussed at the end of this paper to make RMMC method and RMMC+MC method more powerful. (authors)

Li, Z.; Wang, K. [Dept. of Engineering Physics, Tsinghua Univ., Beijing, 100084 (China)

2012-07-01

399

Optimizing large parameter sets in variational quantum Monte Carlo  

NASA Astrophysics Data System (ADS)

We present a technique for optimizing hundreds of thousands of variational parameters in variational quantum Monte Carlo. By introducing iterative Krylov subspace solvers and by multiplying by the Hamiltonian and overlap matrices as they are sampled, we remove the need to construct and store these matrices and thus bypass the most expensive steps of the stochastic reconfiguration and linear method optimization techniques. We demonstrate the effectiveness of this approach by using stochastic reconfiguration to optimize a correlator product state wave function with a Pfaffian reference for four example systems. In two examples on the two dimensional Fermionic Hubbard model, we study 16 and 64 site lattices, recovering energies accurate to 1% in the smaller lattice and predicting particle-hole phase separation in the larger. In two examples involving an ab initio Hamiltonian, we investigate the potential energy curve of a symmetrically dissociated 4×4 hydrogen lattice as well as the singlet-triplet gap in free base porphin. In the hydrogen system we recover 98% or more of the correlation energy at all geometries, while for porphin we compute the gap in a 24 orbital active space to within 0.02 eV of the exact result. The number of variational parameters in these examples ranges from 4×103 to 5×105.

Neuscamman, Eric; Umrigar, C. J.; Chan, Garnet Kin-Lic

2012-01-01

400

A fully coupled Monte Carlo/discrete-ordinates solution to the neutron-transport equation  

SciTech Connect

The neutron transport equation is solved by a hybrid method that iteratively couples regions where deterministic (S{sub N}) and stochastic (Monte Carlo) methods are applied. Unlike previous hybrid methods, the Monte Carlo and S{sub N} regions are fully coupled in the sense that no assumption is made about geometrical separation or decoupling. The hybrid method provides a new means of solving problems involving both optically thick and optically thin regions that neither Monte Carlo nor S{sub N} is well suited for by themselves. The fully coupled Monte Carlo/S{sub N} technique consists of defining spatial and/or energy regions of a problem in which either a Monte Carlo calculation or an S{sub N} calculation is to be performed. The hybrid method has been implemented in the S{sub N} code TWODANT by adding special-purpose Monte Carlo subroutines to calculate the response matrices and volumetric sources, and linkage subroutines to carry out the interface flux iterations. The common angular boundary fluxes are included in the S{sub N} code as interior boundary sources, leaving the logic for the solution of the transport flux unchanged, while, with minor modifications, the diffusion synthetic accelerator remains effective in accelerating the S{sub N} calculations. The special-purpose Monte Carlo routines used are essentially analog, with few variance reduction techniques employed. The hybrid method is capable of solving forward, inhomogeneous source problems in X {minus} Y and R {minus} Z geometries. This capability includes multigroup problems involving upscatter and fission in non-highly multiplying (k{sub eff} {le} .8) systems. The hybrid method has been applied to several simple test problems with good results.

Baker, R.S.

1990-01-01

401

Continuous-time quantum Monte Carlo impurity solvers  

NASA Astrophysics Data System (ADS)

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states. Program summaryProgram title: dmft Catalogue identifier: AEIL_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: ALPS LIBRARY LICENSE version 1.1 No. of lines in distributed program, including test data, etc.: 899 806 No. of bytes in distributed program, including test data, etc.: 32 153 916 Distribution format: tar.gz Programming language: C++ Operating system: The ALPS libraries have been tested on the following platforms and compilers: Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher) MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0) IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers Compaq Tru64 UNIX with Compq C++ Compiler (cxx) SGI IRIX with MIPSpro C++ Compiler (CC) HP-UX with HP C++ Compiler (aCC) Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher) RAM: 10 MB-1 GB Classification: 7.3 External routines: ALPS [1], BLAS/LAPACK, HDF5 Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self-energy and local correlation functions. Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2]. Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper. Running time: 60 s-8 h per iteration.

Gull, Emanuel; Werner, Philipp; Fuchs, Sebastian; Surer, Brigitte; Pruschke, Thomas; Troyer, Matthias

2011-04-01

402

Comparison of effects of copropagated and precomputed atmosphere profiles on Monte Carlo trajectory simulation  

NASA Technical Reports Server (NTRS)

A realization of a stochastic atmosphere model for use in simulations is presented. The model provides pressure, density, temperature, and wind velocity as a function of latitude, longitude, and altitude, and is implemented in a three degree of freedom simulation package. This implementation is used in the Monte Carlo simulation of an aeroassisted orbital transfer maneuver and results are compared to those of a more traditional approach.

Queen, Eric M.; Omara, Thomas M.

1990-01-01

403

A radiating shock evaluated using Implicit Monte Carlo Diffusion  

SciTech Connect

Implicit Monte Carlo [1] (IMC) has been shown to be very expensive when used to evaluate a radiation field in opaque media. Implicit Monte Carlo Diffusion (IMD) [2], which evaluates a spatial discretized diffusion equation using a Monte Carlo algorithm, can be used to reduce the cost of evaluating the radiation field in opaque media [2]. This work couples IMD to the hydrodynamics equations to evaluate opaque diffusive radiating shocks. The Lowrie semi-analytic diffusive radiating shock benchmark[a] is used to verify our implementation of the coupled system of equations. (authors)

Cleveland, M.; Gentile, N. [Lawrence Livermore National Laboratory, P. O. Box 808, Livermore CA 94550 (United States)

2013-07-01

404

Linear Filtering Algorithms for Monte Carlo Simulations.  

NASA Astrophysics Data System (ADS)

Available from UMI in association with The British Library. The thesis consists of two main parts. In the first part, an alternative method of Monte Carlo simulation is presented where the expectation values are calculated by weighting configurations generated according to a multi -dimensional Gaussian distribution. The motivation for this approach is that any inefficiency resulting from not using importance sampling may be compensated by the rapid generation of Gaussian configurations. This is achieved by recursive filtering of arrays of Gaussian random numbers, hence the algorithm is called Linear Filtering. We found that the method offers substantial improvements in computer time for models where the action has a single minimum. To simulate more complicated models, the Gaussian must be replaced by distributions which respect the topology of the problem as well as being amenable to recursive filtering. We found distributions satisfying these requirements for two models with non-trivial topology. The other main problem considered, is the computational study of the planer model. We present a method of simulating the Coulomb gas of vortices for the periodic Gaussian model which is the actual model used by Kosterlitz and Thouless in their analysis of the phase transitions in two-dimensional systems with continuous symmetry. The computational interest of the problem is the long range coupling between the vortices. In our method, the probabilities of a large number of vortex updates are calculated at each step and one update is selected using a procedure where the efficiency is independent of the peaking in the probabilities. The lack of dependence on peaking is in contrast to the usual Heat Bath algorithm and leads to small relaxation times. Our results are in good agreement with the predictions of the Kosterlitz and Thouless theory. We find the critical point at T _{rm c} = 1.39 and the specific heat peak at T = 1.65. The method is applicable to other models with long range coupling. Also considered in this thesis is the determination of the fractal dimension for quantum paths in one and two space dimensions with the aid of Monte Carlo simulations.

Amir-Azizi, Siamak

1990-01-01

405

Monte Carlo simulations of Protein Adsorption  

NASA Astrophysics Data System (ADS)

Amyloidogenic diseases, such as, Alzheimer's are caused by adsorption and aggregation of partially unfolded proteins. Adsorption of proteins is a concern in design of biomedical devices, such as dialysis membranes. Protein adsorption is often accompanied by conformational rearrangements in protein molecules. Such conformational rearrangements are thought to affect many properties of adsorbed protein molecules such as their adhesion strength to the surface, biological activity, and aggregation tendency. It has been experimentally shown that many naturally occurring proteins, upon adsorption to hydrophobic surfaces, undergo a helix to sheet or random coil secondary structural rearrangement. However, to better understand the equilibrium structural complexities of this phenomenon, we have performed Monte Carlo (MC) simulations of adsorption of a four helix bundle, modeled as a lattice protein, and studied the adsorption behavior and equilibrium protein conformations at different temperatures and degrees of surface hydrophobicity. To study the free energy and entropic effects on adsorption, Canonical ensemble MC simulations have been combined with Weighted Histogram Analysis Method(WHAM). Conformational transitions of proteins on surfaces will be discussed as a function of surface hydrophobicity and compared to analogous bulk transitions.

Sharma, Sumit; Kumar, Sanat K.; Belfort, Georges

2008-03-01

406

Monte Carlo simulation of stoquastic Hamiltonians  

E-print Network

Stoquastic Hamiltonians are characterized by the property that their off-diagonal matrix elements in the standard product basis are real and non-positive. Many interesting quantum models fall into this class including the Transverse field Ising Model (TIM), the Heisenberg model on bipartite graphs, and the bosonic Hubbard model. Here we consider the problem of estimating the ground state energy of a local stoquastic Hamiltonian $H$ with a promise that the ground state of $H$ has a non-negligible correlation with some `guiding' state that admits a concise classical description. A formalized version of this problem called Guided Stoquastic Hamiltonian is shown to be complete for the complexity class MA (a probabilistic analogue of NP). To prove this result we employ the Projection Monte Carlo algorithm with a variable number of walkers. Secondly, we show that the ground state and thermal equilibrium properties of the ferromagnetic TIM can be simulated in polynomial time on a classical probabilistic computer. This result is based on the approximation algorithm for the classical ferromagnetic Ising model due to Jerrrum and Sinclair (1993).

Sergey Bravyi

2015-01-08

407

Quantum Monte Carlo studies of solvated systems  

NASA Astrophysics Data System (ADS)

Solvation qualitatively alters the energetics of diverse processes from protein folding to reactions on catalytic surfaces. An explicit description of the solvent in quantum-mechanical calculations requires both a large number of electrons and exploration of a large number of configurations in the phase space of the solvent. These problems can be circumvented by including the effects of solvent through a rigorous classical density-functional description of the liquid environment, thereby yielding free energies and thermodynamic averages directly, while eliminating the need for explicit consideration of the solvent electrons. We have implemented and tested this approach within the CASINO Quantum Monte Carlo code. Our method is suitable for calculations in any basis within CASINO, including b-spline and plane wave trial wavefunctions, and is equally applicable to molecules, surfaces, and crystals. For our preliminary test calculations, we use a simplified description of the solvent in terms of an isodensity continuum dielectric solvation approach, though the method is fully compatible with more reliable descriptions of the solvent we shall employ in the future.

Schwarz, Kathleen; Letchworth Weaver, Kendra; Arias, T. A.; Hennig, Richard G.

2011-03-01

408

Monte Carlo simulation of chromatin stretching  

NASA Astrophysics Data System (ADS)

We present Monte Carlo (MC) simulations of the stretching of a single 30nm chromatin fiber. The model approximates the DNA by a flexible polymer chain with Debye-Hückel electrostatics and uses a two-angle zigzag model for the geometry of the linker DNA connecting the nucleosomes. The latter are represented by flat disks interacting via an attractive Gay-Berne potential. Our results show that the stiffness of the chromatin fiber strongly depends on the linker DNA length. Furthermore, changing the twisting angle between nucleosomes from 90° to 130° increases the stiffness significantly. An increase in the opening angle from 22° to 34° leads to softer fibers for small linker lengths. We observe that fibers containing a linker histone at each nucleosome are stiffer compared to those without the linker histone. The simulated persistence lengths and elastic moduli agree with experimental data. Finally, we show that the chromatin fiber does not behave as an isotropic elastic rod, but its rigidity depends on the direction of deformation: Chromatin is much more resistant to stretching than to bending.

Aumann, Frank; Lankas, Filip; Caudron, Maïwen; Langowski, Jörg

2006-04-01

409

Biofilm growth: a lattice Monte Carlo model  

NASA Astrophysics Data System (ADS)

Biofilms are complex colonies of bacteria that grow in contact with a wall, often in the presence of a flow. In the current work, biofilm growth is investigated using a new two-dimensional lattice Monte Carlo algorithm based on the Bond-Fluctuation Algorithm (BFA). One of the distinguishing characteristics of biofilms, the synthesis and physical properties of the extracellular polymeric substance (EPS) in which the cells are embedded, is explicitly taken into account. Cells are modelled as autonomous closed loops with well-defined mechanical and thermodynamic properties, while the EPS is modelled as flexible polymeric chains. This BFA model allows us to add biologically relevant features such as: the uptake of nutrients; cell growth, division and death; the production of EPS; cell maintenance and hibernation; the generation of waste and the impact of toxic molecules; cell mutation and evolution; cell motility. By tuning the structural, interactional and morphologic parameters of the model, the cell shapes as well as the growth and maturation of various types of biofilm colonies can be controlled.

Tao, Yuguo; Slater, Gary

2011-03-01

410

Monte Carlo simulation of stoquastic Hamiltonians  

E-print Network

Stoquastic Hamiltonians are characterized by the property that their off-diagonal matrix elements in the standard product basis are real and non-positive. Many interesting quantum models fall into this class including the Transverse field Ising Model (TIM), the Heisenberg model on bipartite graphs, and the bosonic Hubbard model. Here we consider the problem of estimating the ground state energy of a local stoquastic Hamiltonian $H$ with a promise that the ground state of $H$ has a non-negligible correlation with some `guiding' state that admits a concise classical description. A formalized version of this problem called Guided Stoquastic Hamiltonian is shown to be complete for the complexity class MA (a probabilistic analogue of NP). To prove this result we employ the Projection Monte Carlo algorithm with a variable number of walkers. Secondly, we show that the ground state and thermal equilibrium properties of the ferromagnetic TIM can be simulated in polynomial time on a classical probabilistic computer. This result is based on the approximation algorithm for the classical ferromagnetic Ising model due to Jerrrum and Sinclair (1993).

Sergey Bravyi

2014-02-10

411

Improved method for implicit Monte Carlo  

SciTech Connect

The Implicit Monte Carlo (IMC) method has been used for over 30 years to analyze radiative transfer problems, such as those encountered in stellar atmospheres or inertial confinement fusion. Reference [2] provided an exact error analysis of IMC for 0-D problems and demonstrated that IMC can exhibit substantial errors when timesteps are large. These temporal errors are inherent in the method and are in addition to spatial discretization errors and approximations that address nonlinearities (due to variation of physical constants). In Reference [3], IMC and four other methods were analyzed in detail and compared on both theoretical grounds and the accuracy of numerical tests. As discussed in, two alternative schemes for solving the radiative transfer equations, the Carter-Forest (C-F) method and the Ahrens-Larsen (A-L) method, do not exhibit the errors found in IMC; for 0-D, both of these methods are exact for all time, while for 3-D, A-L is exact for all time and C-F is exact within a timestep. These methods can yield substantially superior results to IMC.

Brown, F. B. (Forrest B.); Martin, W. R. (William R.)

2001-01-01

412

DETERMINING UNCERTAINTY IN PHYSICAL PARAMETER MEASUREMENTS BY MONTE CARLO SIMULATION  

EPA Science Inventory

A statistical approach, often called Monte Carlo Simulation, has been used to examine propagation of error with measurement of several parameters important in predicting environmental transport of chemicals. These parameters are vapor pressure, water solubility, octanol-water par...

413

Calculating Air Resistance using the Monte Carlo Method  

NSDL National Science Digital Library

Students will discover the terminal velocity to mass relationship and use this information to calculate the air resistance constant. They will evaluate the accuracy of their lab using the Monte Carlo method.

414

An Analysis Tool for Flight Dynamics Monte Carlo Simulations  

E-print Network

and analysis work to understand vehicle operating limits and identify circumstances that lead to mission failure. A Monte Carlo simulation approach that varies a wide range of physical parameters is typically used to generate thousands of test cases...

Restrepo, Carolina 1982-

2011-05-20

415

Bayesian inverse problems with Monte Carlo forward models  

E-print Network

The full application of Bayesian inference to inverse problems requires exploration of a posterior distribution that typically does not possess a standard form. In this context, Markov chain Monte Carlo (MCMC) methods are ...

Bal, Guillaume

416

MODELING LEACHING OF VIRUSES BY THE MONTE CARLO METHOD  

EPA Science Inventory

A predictive screening model was developed for fate and transport of viruses in the unsaturated zone. A database of input parameters allowed Monte Carlo analysis with the model. The resulting kernel densities of predicted attenuation during percolation indicated very ...

417

Combinatorial nuclear level density by a Monte Carlo method  

E-print Network

We present a new combinatorial method for the calculation of the nuclear level density. It is based on a Monte Carlo technique, in order to avoid a direct counting procedure which is generally impracticable for high-A nuclei. The Monte Carlo simulation, making use of the Metropolis sampling scheme, allows a computationally fast estimate of the level density for many fermion systems in large shell model spaces. We emphasize the advantages of this Monte Carlo approach, particularly concerning the prediction of the spin and parity distributions of the excited states, and compare our results with those derived from a traditional combinatorial or a statistical method. Such a Monte Carlo technique seems very promising to determine accurate level densities in a large energy range for nuclear reaction calculations.

N. Cerf

1993-09-14

418

Enhancements in Continuous-Energy Monte Carlo Capabilities in SCALE  

SciTech Connect

Monte Carlo tools in SCALE are commonly used in criticality safety calculations as well as sensitivity and uncertainty analysis, depletion, and criticality alarm system analyses. Recent improvements in the continuous-energy data generated by the AMPX code system and significant advancements in the continuous-energy treatment in the KENO Monte Carlo eigenvalue codes facilitate the use of SCALE Monte Carlo codes to model geometrically complex systems with enhanced solution fidelity. The addition of continuous-energy treatment to the SCALE Monaco code, which can be used with automatic variance reduction in the hybrid MAVRIC sequence, provides significant enhancements, especially for criticality alarm system modeling. This paper describes some of the advancements in continuous-energy Monte Carlo codes within the SCALE code system.

Bekar, Kursat B [ORNL] [ORNL; Celik, Cihangir [ORNL] [ORNL; Wiarda, Dorothea [ORNL] [ORNL; Peplow, Douglas E. [ORNL] [ORNL; Rearden, Bradley T [ORNL] [ORNL; Dunn, Michael E [ORNL] [ORNL

2013-01-01

419

Monte Carlo methods for parallel processing of diffusion equations  

E-print Network

A Monte Carlo algorithm for solving simple linear systems using a random walk is demonstrated and analyzed. The described algorithm solves for each element in the solution vector independently. Furthermore, it is demonstrated ...

Vafadari, Cyrus

2013-01-01

420

Validation of Phonon Physics in the CDMS Detector Monte Carlo  

E-print Network

The SuperCDMS collaboration is a dark matter search effort aimed at detecting the scattering of WIMP dark matter from nuclei in cryogenic germanium targets. The CDMS Detector Monte Carlo (CDMS-DMC) is a simulation tool ...

McCarthy, K. A.

421

Combinatorial geometry domain decomposition strategies for Monte Carlo simulations  

SciTech Connect

Analysis and modeling of nuclear reactors can lead to memory overload for a single core processor when it comes to refined modeling. A method to solve this problem is called 'domain decomposition'. In the current work, domain decomposition algorithms for a combinatorial geometry Monte Carlo transport code are developed on the JCOGIN (J Combinatorial Geometry Monte Carlo transport INfrastructure). Tree-based decomposition and asynchronous communication of particle information between domains are described in the paper. Combination of domain decomposition and domain replication (particle parallelism) is demonstrated and compared with that of MERCURY code. A full-core reactor model is simulated to verify the domain decomposition algorithms using the Monte Carlo particle transport code JMCT (J Monte Carlo Transport Code), which has being developed on the JCOGIN infrastructure. Besides, influences of the domain decomposition algorithms to tally variances are discussed. (authors)

Li, G.; Zhang, B.; Deng, L.; Mo, Z.; Liu, Z.; Shangguan, D.; Ma, Y.; Li, S.; Hu, Z. [Institute of Applied Physics and Computational Mathematics, Beijing, 100094 (China)

2013-07-01

422

Hybrid Monte Carlo Simulation of Graphene on the Hexagonal Lattice  

E-print Network

We present a method for direct hybrid Monte Carlo simulation of graphene on the hexagonal lattice. We compare the results of the simulation with exact results for a unit hexagonal cell system, where the Hamiltonian can be solved analytically.

Brower, R C; Schaich, D

2011-01-01

423

Hybrid Monte Carlo Simulation of Graphene on the Hexagonal Lattice  

E-print Network

We present a method for direct hybrid Monte Carlo simulation of graphene on the hexagonal lattice. We compare the results of the simulation with exact results for a unit hexagonal cell system, where the Hamiltonian can be solved analytically.

R. C. Brower; C. Rebbi; D. Schaich

2011-01-26

424

Monte Carlo simulations of small-angle elastic scattering events  

SciTech Connect

Quantitative interpretation of electron spectroscopy is almost always dependent on the understanding of multiple scattering effects. Monte Carlo simulations are often used to model multiple scattering effects, as this method provides for a conceptually simple framework for incorporating both elastic and inelastic scattering processes. In this paper, we demonstrate that when small-angle deflections are important, diffraction effects become significant, and straightforward Monte Carlo simulations are not expected to be valid. However, a simple modification to the Monte Carlo procedure is presented that uses cluster-derived elastic scattering cross sections rather than those derived from isolated atoms. In this way we can incorporate diffraction effects in the simulations. Results from electron momentum spectroscopy are presented to illustrate these effects. These modified simulations greatly improve the agreement between experiment and theory, and this approach builds a bridge between Monte Carlo and diffraction-based interpretations of experiments.

Vos, M.; Went, M. [Atomic and Molecular Physics Laboratory, Research School of Physical Sciences and Engineering, Australian National University, Canberra ACT 0200 (Australia)

2005-12-15

425

Variance Reduction Techniques for Implicit Monte Carlo Simulations  

E-print Network

The Implicit Monte Carlo (IMC) method is widely used for simulating thermal radiative transfer and solving the radiation transport equation. During an IMC run a grid network is constructed and particles are sourced into the problem to simulate...

Landman, Jacob Taylor

2013-09-19

426

Parallel Fission Bank Algorithms in Monte Carlo Criticality Calculations  

E-print Network

In this work we describe a new method for parallelizing the source iterations in a Monte Carlo criticality calculation. Instead of having one global fission bank that needs to be synchronized, as is traditionally done, our ...

Romano, Paul Kollath

427

Implementation of stratified sampling for Monte Carlo applications  

SciTech Connect

Stratified sampling is used in Monte Carlo calculations to take advantage of certain known aspects of probability distributions. The sampling region is subdivided into discrete subregions, each of which is sampled a preassigned number of times. Stratified sampling, also known as quota sampling, has been known for decades, but its implementation into major production Monte Carlo computer codes has been limited. It was used in the SAM code and in the French TRIPOLI code. The authors recently implemented and tested stratified sampling for the distance-to-collision estimate in the Monte Carlo MCNP code. Not only is this a significant enhancement of a major Monte Carlo computer code, but also the authors believe they are presenting the first published empirical measurement of the stratified sampling effect.

Brown, R.S. Jr.; Hendricks, J.S.

1987-01-01

428

Development of Monte Carlo Capability for Orion Parachute Simulations  

NASA Technical Reports Server (NTRS)

Parachute test programs employ Monte Carlo simulation techniques to plan testing and make critical decisions related to parachute loads, rate-of-descent, or other parameters. This paper describes the development and use of a MATLAB-based Monte Carlo tool for three parachute drop test simulations currently used by NASA. The Decelerator System Simulation (DSS) is a legacy 6 Degree-of-Freedom (DOF) simulation used to predict parachute loads and descent trajectories. The Decelerator System Simulation Application (DSSA) is a 6-DOF simulation that is well suited for modeling aircraft extraction and descent of pallet-like test vehicles. The Drop Test Vehicle Simulation (DTVSim) is a 2-DOF trajectory simulation that is convenient for quick turn-around analysis tasks. These three tools have significantly different software architectures and do not share common input files or output data structures. Separate Monte Carlo tools were initially developed for each simulation. A recently-developed simulation output structure enables the use of the more sophisticated DSSA Monte Carlo tool with any of the core-simulations. The task of configuring the inputs for the nominal simulation is left to the existing tools. Once the nominal simulation is configured, the Monte Carlo tool perturbs the input set according to dispersion rules created by the analyst. These rules define the statistical distribution and parameters to be applied to each simulation input. Individual dispersed parameters are combined to create a dispersed set of simulation inputs. The Monte Carlo tool repeatedly executes the core-simulation with the dispersed inputs and stores the results for analysis. The analyst may define conditions on one or more output parameters at which to collect data slices. The tool provides a versatile interface for reviewing output of large Monte Carlo data sets while preserving the capability for detailed examination of individual dispersed trajectories. The Monte Carlo tool described in this paper has proven useful in planning several Crew Exploration Vehicle parachute tests.

Moore, James W.

2011-01-01

429

A MonteCarlo Algorithm for Estimating the N. Karmarkar  

E-print Network

A Monte­Carlo Algorithm for Estimating the Permanent N. Karmarkar Bell Labs, Murray Hill, N.J. R with 0­1 valued entries, and let per(A) be the per­ manent of A. We describe a Monte­Carlo algorithm of a bipartite graph, H = (X; Y; E) where X corresponds 1 #12; to the rows in A, Y to the columns in A, and A ij

Luby, Michael

430

Sufficient conditions for fast quasi-Monte Carlo convergence  

Microsoft Academic Search

We study the approximation of d-dimensional integrals. We present sucient condi- tions for fast quasi-Monte Carlo convergence. They apply to isotropic and non-isotropic problems and, in particular, to a number of problems in computational finance. We show that the convergence rate of quasi-Monte Carlo is of order n 1+p{logn} 1\\/2 with p 0. This is a worst case result. Compared

Anargyros Papageorgiou

2003-01-01

431

Monte Carlo simulation and measurement of nanoscale n-MOSFETs  

Microsoft Academic Search

The output characteristics of state-of-the-art n-MOSFETs with effective channel lengths of 40 and 60 nm have been measured and compared with full-band Monte Carlo simulations. The device structures are obtained by process simulation based on comprehensive secondary ion mass spectroscopy and capacitance-voltage measurements. Good agreement between the measured output characteristics and the full-band Monte Carlo simulations is found without any

F. M. Bufler; Yoshinori Asahi; Hisao Yoshimura; Christoph Zechner; A. Schenk; Wolfgang Fichtner

2003-01-01

432

Monte Carlo simulation of energy spectra for 123I imaging  

Microsoft Academic Search

123I is a radionuclide frequently used in nuclear medicine imaging. The image formed by the 159 keV photopeak includes a considerable scatter component due to high energy gamma-ray emission. In order to evaluate the fraction of scattered photons, a Monte Carlo simulation of a scintillation camera used for 123I imaging was undertaken. The Monte Carlo code consists of two modules,

Minoru Tanaka; Shuzo Uehara; Akihiro Kojima; Masanori Matsumoto

2007-01-01

433

Monte Carlo Based Design of Photonic Processes in Azopolymers  

Microsoft Academic Search

Monte Carlo kinetics of diffraction efficiency evolution in a process of a pulsed diffraction grating inscription in a model system consisting of a polymer doped with azo-dye is presented. A comparison between simulations and degenerate two-wave mixing (DTWM) experiment is given. A good qualitative agreement of those results supports the concept of Monte Carlo based analysis and design of temperature-dependent

A. C. Mitus; G. Pawlik; B. Sahraoui; A. Miniewicz; F. Kajzar

2006-01-01

434

Optimally combining sampling tech-niques for Monte Carlo rendering  

Microsoft Academic Search

Monte Carlo integration is a powerful technique for the evaluation of difficult integrals. Applications in rendering include distribution ray tracing, Monte Carlo path tracing, and form-factor computation for radiosity methods. In these cases variance can often be signifi-cantly reduced by drawing samples from several distributions, each designed to sample well some difficult aspect of the integrand. Nor-mally this is done

E. Veach; L. Guibas

1994-01-01

435

Monte Carlo for top background at the Tevatron  

E-print Network

We review the use of Monte Carlo simulation to model backgrounds to top signal at the Tevatron experiments, CDF and D0, as well as the relevant measurements done by the experiments. We'll concentrate on the modeling of W and Z boson production in association with jets, in particular heavy flavor jets, and also comment on the Tevatron experience using matched Monte Carlo.

Amnon Harel

2008-07-25

436

Study of the Transition Flow Regime using Monte Carlo Methods  

NASA Technical Reports Server (NTRS)

This NASA Cooperative Agreement presents a study of the Transition Flow Regime Using Monte Carlo Methods. The topics included in this final report are: 1) New Direct Simulation Monte Carlo (DSMC) procedures; 2) The DS3W and DS2A Programs; 3) Papers presented; 4) Miscellaneous Applications and Program Modifications; 5) Solution of Transitional Wake Flows at Mach 10; and 6) Turbulence Modeling of Shock-Dominated Fows with a k-Enstrophy Formulation.

Hassan, H. A.

1999-01-01

437

Monte Carlo simulations of the transport of sputtered particles  

NASA Astrophysics Data System (ADS)

Program SPATS models the transport of neutral particles during magnetron sputtering deposition. The 3D Monte Carlo simulation provides information about spatial distribution of the fluxes, density of the sputtered particles in the chamber glow discharge area, and kinetic energy distribution of the arrival flux. Collision events are modelled by scattering in Biersack's potential, Lennard-Jones potential, or by binary hard sphere collision approximation. The code has an interface for Monte Carlo TRIM simulated results of the sputtered particles.

Macàk, Karol; Macàk, Peter; Helmersson, Ulf

1999-08-01

438

Monte Carlo study of a Cyberknife stereotactic radiosurgery system  

Microsoft Academic Search

This study investigated small-field dosimetry for a Cyberknife stereotactic radiosurgery system using Monte Carlo simulations. The EGSnrc\\/BEAMnrc Monte Carlo code was used to simulate the Cyberknife treatment head, and the DOSXYZnrc code was implemented to calculate central axis depth-dose curves, off-axis dose profiles, and relative output factors for various circular collimator sizes of 5 to 60 mm. Water-to-air stopping power

Fujio Araki; Fujio

2006-01-01

439

Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms  

Microsoft Academic Search

IntroductionThe goal of these lectures is to give an introduction to current research on MonteCarlo methods in statistical mechanics and quantum field theory, with an emphasis on:1) the conceptual foundations of the method, including the possible dangers andmisuses, and the correct use of statistical error analysis; and2) new Monte Carlo algorithms for problems in critical phenomena and quantumfield theory, aimed

Alan D. Sokal

1996-01-01

440

American Option Pricing on Reconfigurable Hardware Using Least-Squares Monte Carlo Method  

E-print Network

American Option Pricing on Reconfigurable Hardware Using Least-Squares Monte Carlo Method Xiang using the simple Monte Carlo method. A number of extended Monte Carlo methods have been published, the Quasi-Monte Carlo method is adopted for stock price paths generation. Our real FPGA hardware

Arslan, Tughrul

441

Monte Carlo study of the Baxter-Wu model Nir Schreiber and Dr. Joan Adler  

E-print Network

Monte Carlo study of the Baxter-Wu model Nir Schreiber and Dr. Joan Adler Monte Carlo study of the Baxter-Wu model ­ p.1/40 #12;Outline Theory of phase transitions, Monte Carlo simulations and finite size scaling Landau-Wang algorithm Results Summary Monte Carlo study of the Baxter-Wu model ­ p.2/40 #12;Phase

Adler, Joan

442

A Monte Carlo method to compute the exchange coefficient in the double porosity model  

E-print Network

A Monte Carlo method to compute the exchange coefficient in the double porosity model Fabien: Monte Carlo methods, double porosity model, ran- dom walk on squares, fissured media AMS Classification: 76S05 (65C05 76M35) Published in Monte Carlo Methods Appl.. Proc. of Monte Carlo and probabilistic

Paris-Sud XI, Université de

443

Monte Carlo EM for Generalized Linear Mixed Models using Randomized Spherical Radial  

E-print Network

Monte Carlo EM for Generalized Linear Mixed Models using Randomized Spherical Radial Integration by Monte Carlo methods. However, in practice, the Monte Carlo sample sizes required for convergence for such methods. One solution is to use Monte Carlo approximation, as proposed by Wei and Tanner (1990

Booth, James

444

Monte Carlo Simulation of Electrodeposition of Copper: A Multistep Free Energy Calculation  

E-print Network

Monte Carlo Simulation of Electrodeposition of Copper: A Multistep Free Energy Calculation S such as continuum Monte Carlo, kinetic Monte Carlo (KMC), and molecular dynamics have been used for simulating is very time-consuming. Thus a less time-consuming and novel multistep continuum Monte Carlo simulation

Subramanian, Venkat

445

1. Area: Monte Carlo Methods [up to 2 projects available] Proposer: Geoff Nicholls  

E-print Network

1. Area: Monte Carlo Methods [up to 2 projects available] Proposer: Geoff Nicholls This is a project on Monte-Carlo methods. It can be treated as a project in Applied Probability (the approach taken developments in Monte Carlo Methods themselves, or in applications of Monte Carlo in Applied probability

Goldschmidt, Christina

446

Perturbation Monte Carlo methods for tissue structure alterations  

PubMed Central

This paper describes an extension of the perturbation Monte Carlo method to model light transport when the phase function is arbitrarily perturbed. Current perturbation Monte Carlo methods allow perturbation of both the scattering and absorption coefficients, however, the phase function can not be varied. The more complex method we develop and test here is not limited in this way. We derive a rigorous perturbation Monte Carlo extension that can be applied to a large family of important biomedical light transport problems and demonstrate its greater computational efficiency compared with using conventional Monte Carlo simulations to produce forward transport problem solutions. The gains of the perturbation method occur because only a single baseline Monte Carlo simulation is needed to obtain forward solutions to other closely related problems whose input is described by perturbing one or more parameters from the input of the baseline problem. The new perturbation Monte Carlo methods are tested using tissue light scattering parameters relevant to epithelia where many tumors originate. The tissue model has parameters for the number density and average size of three classes of scatterers; whole nuclei, organelles such as lysosomes and mitochondria, and small particles such as ribosomes or large protein complexes. When these parameters or the wavelength is varied the scattering coefficient and the phase function vary. Perturbation calculations give accurate results over variations of ?15–25% of the scattering parameters. PMID:24156056

Nguyen, Jennifer; Hayakawa, Carole K.; Mourant, Judith R.; Spanier, Jerome

2013-01-01

447

DPEMC: A Monte Carlo for double diffraction  

NASA Astrophysics Data System (ADS)

We extend the POMWIG Monte Carlo generator developed by B. Cox and J. Forshaw, to include new models of central production through inclusive and exclusive double Pomeron exchange in proton-proton collisions. Double photon exchange processes are described as well, both in proton-proton and heavy-ion collisions. In all contexts, various models have been implemented, allowing for comparisons and uncertainty evaluation and enabling detailed experimental simulations. Program summaryTitle of the program:DPEMC, version 2.4 Catalogue identifier: ADVF Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVF Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer: any computer with the FORTRAN 77 compiler under the UNIX or Linux operating systems Operating system: UNIX; Linux Programming language used: FORTRAN 77 High speed storage required:<25 MB No. of lines in distributed program, including test data, etc.: 71 399 No. of bytes in distributed program, including test data, etc.: 639 950 Distribution format: tar.gz Nature of the physical problem: Proton diffraction at hadron colliders can manifest itself in many forms, and a variety of models exist that attempt to describe it [A. Bialas, P.V. Landshoff, Phys. Lett. B 256 (1991) 540; A. Bialas, W. Szeremeta, Phys. Lett. B 296 (1992) 191; A. Bialas, R.A. Janik, Z. Phys. C 62 (1994) 487; M. Boonekamp, R. Peschanski, C. Royon, Phys. Rev. Lett. 87 (2001) 251806; Nucl. Phys. B 669 (2003) 277; R. Enberg, G. Ingelman, A. Kissavos, N. Timneanu, Phys. Rev. Lett. 89 (2002) 081801; R. Enberg, G. Ingelman, L. Motyka, Phys. Lett. B 524 (2002) 273; R. Enberg, G. Ingelman, N. Timneanu, Phys. Rev. D 67 (2003) 011301; B. Cox, J. Forshaw, Comput. Phys. Comm. 144 (2002) 104; B. Cox, J. Forshaw, B. Heinemann, Phys. Lett. B 540 (2002) 26; V. Khoze, A. Martin, M. Ryskin, Phys. Lett. B 401 (1997) 330; Eur. Phys. J. C 14 (2000) 525; Eur. Phys. J. C 19 (2001) 477; Erratum, Eur. Phys. J. C 20 (2001) 599; Eur. Phys. J. C 23 (2002) 311]. This program implements some of the more significant ones, enabling the simulation of central particle production through color singlet exchange between interacting protons or antiprotons. Method of solution: The Monte Carlo method is used to simulate all elementary 2?2 and 2?1 processes available in HERWIG. The color singlet exchanges implemented in DPEMC are implemented as functions reweighting the photon flux already present in HERWIG. Restriction on the complexity of the problem: The program relying extensively on HERWIG, the limitations are the same as in [G. Marchesini, B.R. Webber, G. Abbiendi, I.G. Knowles, M.H. Seymour, L. Stanco, Comput. Phys. Comm. 67 (1992) 465; G. Corcella, I.G. Knowles, G. Marchesini, S. Moretti, K. Odagiri, P. Richardson, M. Seymour, B. Webber, JHEP 0101 (2001) 010]. Typical running time: Approximate times on a 800 MHz Pentium III: 5-20 min per 10 000 unweighted events, depending on the process under consideration.

Boonekamp, M.; Kúcs, T.

2005-05-01

448

A Monte Carlo solution of heat conduction and Poisson equations  

Microsoft Academic Search

A Monte Carlo method is developed for solving the heat conduction, Poisson, and Laplace equations. The method is based on properties of Brownian motion and Ito processes, the Ito formula for differentiable functions of these processes, and the similarities between the generator of Ito processes and the differential operators of these equations. The proposed method is similar to current Monte

M. Grigoriu

2000-01-01

449

Markov Chain Monte Carlo Methods in Biostatistics Andrew Gelman  

E-print Network

Markov Chain Monte Carlo Methods in Biostatistics Andrew Gelman Department of Statistics Columbia May 21, 1996 1 Introduction Appropriate models in biostatistics are often quite complicated, re ecting in biostatistics. These readers can use this article as an introduction to the ways in which Markov chain Monte

Gelman, Andrew

450

4 Monte Carlo Methods in Classical Statistical Physics  

E-print Network

4 Monte Carlo Methods in Classical Statistical Physics Wolfhard Janke Institut f¨ur Theoretische update algorithms (Metropolis, heat-bath, Glauber). Then methods for the statistical analysis of the thus Carlo Methods in Classical Statistical Physics, Lect. Notes Phys. 739, 79­140 (2008) DOI 10

Janke, Wolfhard

451

Lattice Monte Carlo simulations of polymer melts.  

PubMed

We use Monte Carlo simulations to study polymer melts consisting of fully flexible and moderately stiff chains in the bond fluctuation model at a volume fraction 0.5. In order to reduce the local density fluctuations, we test a pre-packing process for the preparation of the initial configurations of the polymer melts, before the excluded volume interaction is switched on completely. This process leads to a significantly faster decrease of the number of overlapping monomers on the lattice. This is useful for simulating very large systems, where the statistical properties of the model with a marginally incomplete elimination of excluded volume violations are the same as those of the model with strictly excluded volume. We find that the internal mean square end-to-end distance for moderately stiff chains in a melt can be very well described by a freely rotating chain model with a precise estimate of the bond-bond orientational correlation between two successive bond vectors in equilibrium. The plot of the probability distributions of the reduced end-to-end distance of chains of different stiffness also shows that the data collapse is excellent and described very well by the Gaussian distribution for ideal chains. However, while our results confirm the systematic deviations between Gaussian statistics for the chain structure factor Sc(q) [minimum in the Kratky-plot] found by Wittmer et al. [EPL 77, 56003 (2007)] for fully flexible chains in a melt, we show that for the available chain length these deviations are no longer visible, when the chain stiffness is included. The mean square bond length and the compressibility estimated from collective structure factors depend slightly on the stiffness of the chains. PMID:25527957

Hsu, Hsiao-Ping

2014-12-21

452

kmos: A lattice kinetic Monte Carlo framework  

NASA Astrophysics Data System (ADS)

Kinetic Monte Carlo (kMC) simulations have emerged as a key tool for microkinetic modeling in heterogeneous catalysis and other materials applications. Systems, where site-specificity of all elementary reactions allows a mapping onto a lattice of discrete active sites, can be addressed within the particularly efficient lattice kMC approach. To this end we describe the versatile kmos software package, which offers a most user-friendly implementation, execution, and evaluation of lattice kMC models of arbitrary complexity in one- to three-dimensional lattice systems, involving multiple active sites in periodic or aperiodic arrangements, as well as site-resolved pairwise and higher-order lateral interactions. Conceptually, kmos achieves a maximum runtime performance which is essentially independent of lattice size by generating code for the efficiency-determining local update of available events that is optimized for a defined kMC model. For this model definition and the control of all runtime and evaluation aspects kmos offers a high-level application programming interface. Usage proceeds interactively, via scripts, or a graphical user interface, which visualizes the model geometry, the lattice occupations and rates of selected elementary reactions, while allowing on-the-fly changes of simulation parameters. We demonstrate the performance and scaling of kmos with the application to kMC models for surface catalytic processes, where for given operation conditions (temperature and partial pressures of all reactants) central simulation outcomes are catalytic activity and selectivities, surface composition, and mechanistic insight into the occurrence of individual elementary processes in the reaction network.

Hoffmann, Max J.; Matera, Sebastian; Reuter, Karsten

2014-07-01

453

Monte Carlo role in radiobiological modelling of radiotherapy outcomes  

NASA Astrophysics Data System (ADS)

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.

El Naqa, Issam; Pater, Piotr; Seuntjens, Jan

2012-06-01

454

Monte Carlo Simulation of the Law of the Maximum of a Levy Process Monte Carlo Simulation of the Law of the Maximum of a  

E-print Network

1/ 17 Monte Carlo Simulation of the Law of the Maximum of a L´evy Process Monte Carlo Simulation of Mathematical Sciences, University of Bath #12;2/ 17 Monte Carlo Simulation of the Law of the Maximum of a L´evy Process Motivation #12;2/ 17 Monte Carlo Simulation of the Law of the Maximum of a L´evy Process

455

A rare event sampling method for diffusion Monte Carlo using smart darting  

NASA Astrophysics Data System (ADS)

We identify a set of multidimensional potential energy surfaces sufficiently complex to cause both the classical parallel tempering and the guided or unguided diffusion Monte Carlo methods to converge too inefficiently for practical applications. The mathematical model is constructed as a linear combination of decoupled Double Wells [(DDW)n]. We show that the set (DDW)n provides a serious test for new methods aimed at addressing rare event sampling in stochastic simulations. Unlike the typical numerical tests used in these cases, the thermodynamics and the quantum dynamics for (DDW)n can be solved deterministically. We use the potential energy set (DDW)n to explore and identify methods that can enhance the diffusion Monte Carlo algorithm. We demonstrate that the smart darting method succeeds at reducing quasiergodicity for n ? 100 using just 1 × 106 moves in classical simulations (DDW)n. Finally, we prove that smart darting, when incorporated into the regular or the guided diffusion Monte Carlo algorithm, drastically improves its convergence. The new method promises to significantly extend the range of systems computationally tractable by the diffusion Monte Carlo algorithm.

Roberts, K.; Sebsebie, R.; Curotto, E.

2012-02-01

456

On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods.  

PubMed

We present a case-study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers and can be thought of as prototypes of the next generation of many-core processors. For certain classes of population-based Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multi-core processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we nd speedups from 35 to 500 fold over conventional single-threaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modelling into complex data rich domains through the availability of cheap and accessible many-core computation. We believe the speedup we observe should motivate wider use of parallelizable simulation methods and greater methodological attention to their design. PMID:22003276

Lee, Anthony; Yau, Christopher; Giles, Michael B; Doucet, Arnaud; Holmes, Christopher C

2010-12-01

457

Coupling Deterministic and Monte Carlo Transport Methods for the Simulation of Gamma-Ray Spectroscopy Scenarios  

SciTech Connect

Radiation transport modeling methods used in the radiation detection community fall into one of two broad categories: stochastic (Monte Carlo) and deterministic. Monte Carlo methods are typically the tool of choice for simulating gamma-ray spectrometers operating in homeland and national security settings (e.g. portal monitoring of vehicles or isotope identification using handheld devices), but deterministic codes that discretize the linear Boltzmann transport equation in space, angle, and energy offer potential advantages in computational efficiency for many complex radiation detection problems. This paper describes the development of a scenario simulation framework based on deterministic algorithms. Key challenges include: formulating methods to automatically define an energy group structure that can support modeling of gamma-ray spectrometers ranging from low to high resolution; combining deterministic transport algorithms (e.g. ray-tracing and discrete ordinates) to mitigate ray effects for a wide range of problem types; and developing efficient and accurate methods to calculate gamma-ray spectrometer response functions from the deterministic angular flux solutions. The software framework aimed at addressing these challenges is described and results from test problems that compare coupled deterministic-Monte Carlo methods and purely Monte Carlo approaches are provided.

Smith, Leon E.; Gesh, Christopher J.; Pagh, Richard T.; Miller, Erin A.; Shaver, Mark W.; Ashbaker, Eric D.; Batdorf, Michael T.; Ellis, J. E.; Kaye, William R.; McConn, Ronald J.; Meriwether, George H.; Ressler, Jennifer J.; Valsan, Andrei B.; Wareing, Todd A.

2008-10-31

458

Monte Carlo Methods and Appl., Vol. 11, No. 1, pp. 39 55 (2005) Grid-based Quasi-Monte Carlo Applications  

E-print Network

Monte Carlo Methods and Appl., Vol. 11, No. 1, pp. 39 ­ 55 (2005) c VSP 2005 Grid-based Quasi-Monte -- In this paper, we extend the techniques used in Grid-based Monte Carlo appli- cations to Grid-based quasi-Monte in quasirandom sequences prevents us from applying many of our Grid-based Monte Carlo techniques to Grid- based

Li, Yaohang

459

Analysis of Large-scale Grid-based Monte Carlo Applications Analysis of Large-scale Grid-based Monte Carlo  

E-print Network

Analysis of Large-scale Grid-based Monte Carlo Applications Analysis of Large-scale Grid-based Monte Carlo Applications Yaohang Li and Michael Mascagni Department of Computer Science and School-based Monte Carlo Applications Yaohang Li* Department of Computer Science and School of Computational Science

Mascagni, Michael

460

Probability Forecasting Using Monte Carlo Simulation  

NASA Astrophysics Data System (ADS)

Space Situational Awareness (SSA) is defined as the knowledge and characterization of all aspects of space. SSA is now a fundamental and critical component of space operations. Increased dependence on our space assets has in turn lead to a greater need for accurate, near real-time knowledge of all space activities. With the growth of the orbital debris population, satellite operators are performing collision avoidance maneuvers more frequently. Frequent maneuver execution expends fuel and reduces the operational lifetime of the spacecraft. Thus the need for new, more sophisticated collision threat characterization methods must be implemented. The collision probability metric is used operationally to quantify the collision risk. The collision probability is typically calculated days into the future, so that high risk and potential high risk conjunction events are identified early enough to develop an appropriate course of action. As the time horizon to the conjunction event is reduced, the collision probability changes. A significant change in the collision probability will change the satellite mission stakeholder's course of action. So constructing a method for estimating how the collision probability will evolve improves operations by providing satellite operators with a new piece of information, namely an estimate or 'forecast' of how the risk will change as time to the event is reduced. Collision probability forecasting is a predictive process where the future risk of a conjunction event is estimated. The method utilizes a Monte Carlo simulation that produces a likelihood distribution for a given collision threshold. Using known state and state uncertainty information, the simulation generates a set possible trajectories for a given space object pair. Each new trajectory produces a unique event geometry at the time of close approach. Given state uncertainty information for both objects, a collision probability value can be computed for every trail. This yields a collision probability distribution given known, predicted uncertainty. This paper presents the details of the collision probability forecasting method. We examine various conjunction event scenarios and numerically demonstrate the utility of this approach in typical event scenarios. We explore the utility of a probability-based track scenario simulation that models expected tracking data frequency as the tasking levels are increased. The resulting orbital uncertainty is subsequently used in the forecasting algorithm.

Duncan, M.; Frisbee, J.; Wysack, J.

2014-09-01

461

TOPICAL REVIEW: Monte Carlo modelling of external radiotherapy photon beams  

NASA Astrophysics Data System (ADS)

An essential requirement for successful radiation therapy is that the discrepancies between dose distributions calculated at the treatment planning stage and those delivered to the patient are minimized. An important component in the treatment planning process is the accurate calculation of dose distributions. The most accurate way to do this is by Monte Carlo calculation of particle transport, first in the geometry of the external or internal source followed by tracking the transport and energy deposition in the tissues of interest. Additionally, Monte Carlo simulations allow one to investigate the influence of source components on beams of a particular type and their contaminant particles. Since the mid 1990s, there has been an enormous increase in Monte Carlo studies dealing specifically with the subject of the present review, i.e., external photon beam Monte Carlo calculations, aided by the advent of new codes and fast computers. The foundations for this work were laid from the late 1970s until the early 1990s. In this paper we will review the progress made in this field over the last 25 years. The review will be focused mainly on Monte Carlo modelling of linear accelerator treatment heads but sections will also be devoted to kilovoltage x-ray units and 60Co teletherapy sources.

Verhaegen, Frank; Seuntjens, Jan

2003-11-01

462

Communication: Monte Carlo calculation of the exchange energy Roi Baer and Daniel Neuhauser  

E-print Network

Communication: Monte Carlo calculation of the exchange energy Roi Baer and Daniel Neuhauser OF CHEMICAL PHYSICS 137, 051103 (2012) Communication: Monte Carlo calculation of the exchange energy Roi Baer1 Monte Carlo (MC) methods for calculating the exchange energy. The Monte a)E-mail: roi.baer@huji.ac.il. b

Baer, Roi

463

Rao-Blackwellised Interacting Markov Chain Monte Carlo for Electromagnetic Scattering Inversion  

NASA Astrophysics Data System (ADS)

The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from the global EM scattering measurement, at various incidences and wave frequencies. This large scale ill-posed inverse problem is explored by an intensive exploitation of an efficient 2D Maxwell solver, distributed on High Performance Computing (HPC) machines. Applied to a large training data set, a statistical analysis reduces the problem to a simpler probabilistic metamodel, on which Bayesian inference can be performed. Considering the radioelectric properties as a dynamic stochastic process, evolving in function of the frequency, it is shown how advanced Markov Chain Monte Carlo methods, called Sequential Monte Carlo (SMC) or interacting particles, can provide estimations of the EM properties of each material, and their associated uncertainties.

Giraud, F.; Minvielle, P.; Sancandi, M.; Del Moral, P.

2012-09-01

464

A New Monte Carlo Method and Its Implications for Generalized Cluster Algorithms  

E-print Network

We describe a novel switching algorithm based on a ``reverse'' Monte Carlo method, in which the potential is stochastically modified before the system configuration is moved. This new algorithm facilitates a generalized formulation of cluster-type Monte Carlo methods, and the generalization makes it possible to derive cluster algorithms for systems with both discrete and continuous degrees of freedom. The roughening transition in the sine-Gordon model has been studied with this method, and high-accuracy simulations for system sizes up to $1024^2$ were carried out to examine the logarithmic divergence of the surface roughness above the transition temperature, revealing clear evidence for universal scaling of the Kosterlitz-Thouless type.

C. H. Mak; Arun K. Sharma

2007-04-12

465

Residual Monte Carlo high-order solver for Moment-Based Accelerated Thermal Radiative Transfer equations  

NASA Astrophysics Data System (ADS)

In this article we explore the possibility of replacing Standard Monte Carlo (SMC) transport sweeps within a Moment-Based Accelerated Thermal Radiative Transfer (TRT) algorithm with a Residual Monte Carlo (RMC) formulation. Previous Moment-Based Accelerated TRT implementations have encountered trouble when stochastic noise from SMC transport sweeps accumulates over several iterations and pollutes the low-order system. With RMC we hope to significantly lower the build-up of statistical error at a much lower cost. First, we display encouraging results for a zero-dimensional test problem. Then, we demonstrate that we can achieve a lower degree of error in two one-dimensional test problems by employing an RMC transport sweep with multiple orders of magnitude fewer particles per sweep. We find that by reformulating the high-order problem, we can compute more accurate solutions at a fraction of the cost.

Willert, Jeffrey; Park, H.

2014-11-01

466

Quantum Monte Carlo Simulations of Solid 4 P.A. Whitlock1  

E-print Network

Quantum Monte Carlo Simulations of Solid 4 He P.A. Whitlock1 and S.A. Vitiello2 1 Computer Carlo calculations at zero temperature; diffusion Monte Carlo, and finally, the finite temperature path integral Monte Carlo method. A brief introduction to the technique will be given followed by a discussion

Whitlock, Paula

467

A Quantum Monte Carlo Method at Fixed Energy  

E-print Network

In this paper we explore new ways to study the zero temperature limit of quantum statistical mechanics using Quantum Monte Carlo simulations. We develop a Quantum Monte Carlo method in which one fixes the ground state energy as a parameter. The Hamiltonians we consider are of the form $H=H_{0}+\\lambda V$ with ground state energy E. For fixed $H_{0}$ and V, one can view E as a function of $\\lambda$ whereas we view $\\lambda$ as a function of E. We fix E and define a path integral Quantum Monte Carlo method in which a path makes no reference to the times (discrete or continuous) at which transitions occur between states. For fixed E we can determine $\\lambda(E)$ and other ground state properties of H.

Edward Farhi; Jeffrey Goldstone; David Gosset; Harvey B. Meyer

2009-12-21

468

Efficiency of Monte Carlo Sampling in Chaotic Systems  

E-print Network

In this paper we investigate how the complexity of chaotic phase spaces affect the efficiency of importance sampling Monte Carlo simulations. We focus on a flat-histogram simulation of the distribution of finite-time Lyapunov exponent in a simple chaotic system and obtain analytically that the computational effort of the simulation: (i) scales polynomially with the finite-time, a tremendous improvement over the exponential scaling obtained in usual uniform sampling simulations; and (ii) the polynomial scaling is sub-optimal, a phenomenon known as critical slowing down. We show that critical slowing down appears because of the limited possibilities to issue a local proposal on the Monte Carlo procedure in chaotic systems. These results remain valid in other methods and show how generic properties of chaotic systems limit the efficiency of Monte Carlo simulations.

Jorge C. Leitão; Eduardo G. Altmann; J. M. Viana Parente Lopes

2014-07-20

469

The Monte Carlo method in quantum field theory  

E-print Network

This series of six lectures is an introduction to using the Monte Carlo method to carry out nonperturbative studies in quantum field theories. Path integrals in quantum field theory are reviewed, and their evaluation by the Monte Carlo method with Markov-chain based importance sampling is presented. Properties of Markov chains are discussed in detail and several proofs are presented, culminating in the fundamental limit theorem for irreducible Markov chains. The example of a real scalar field theory is used to illustrate the Metropolis-Hastings method and to demonstrate the effectiveness of an action-preserving (microcanonical) local updating algorithm in reducing autocorrelations. The goal of these lectures is to provide the beginner with the basic skills needed to start carrying out Monte Carlo studies in quantum field theories, as well as to present the underlying theoretical foundations of the method.

Colin Morningstar

2007-02-20

470

Efficient Monte Carlo characterization of quantum operations for qudits  

E-print Network

For qubits, Monte Carlo estimation of the average fidelity of Clifford unitaries is efficient -- it requires a number of experiments that is independent of the number $n$ of qubits and classical computational resources that scale only polynomially in $n$. Here, we identify the requirements for efficient Monte Carlo estimation and the corresponding properties of the measurement operator basis when replacing two-level qubits by $p$-level qudits. Our analysis illuminates the intimate connection between mutually unbiased measurements and the existence of unitaries that can be characterized efficiently. It allows us to propose a 'hierarchy' of generalizations of the standard Pauli basis from qubits to qudits according to the associated scaling of resources required in Monte Carlo estimation of the average fidelity.

Giulia Gualdi; David Licht; Daniel M. Reich; Christiane P. Koch

2014-04-06

471

Skin image reconstruction using Monte Carlo based color generation  

NASA Astrophysics Data System (ADS)

We propose a novel method of skin image reconstruction based on color generation using Monte Carlo simulation of spectral reflectance in the nine-layered skin tissue model. The RGB image and spectral reflectance of human skin are obtained by RGB camera and spectrophotometer, respectively. The skin image is separated into the color component and texture component. The measured spectral reflectance is used to evaluate scattering and absorption coefficients in each of the nine layers which are necessary for Monte Carlo simulation. Various skin colors are generated by Monte Carlo simulation of spectral reflectance in given conditions for the nine-layered skin tissue model. The new color component is synthesized to the original texture component to reconstruct the skin image. The method is promising for applications in the fields of dermatology and cosmetics.

Aizu, Yoshihisa; Maeda, Takaaki; Kuwahara, Tomohiro; Hirao, Tetsuji

2010-11-01

472

LCG MCDB -- a Knowledgebase of Monte Carlo Simulated Events  

E-print Network

In this paper we report on LCG Monte Carlo Data Base (MCDB) and software which has been developed to operate MCDB. The main purpose of the LCG MCDB project is to provide a storage and documentation system for sophisticated event samples simulated for the LHC collaborations by experts. In many cases, the modern Monte Carlo simulation of physical processes requires expert knowledge in Monte Carlo generators or significant amount of CPU time to produce the events. MCDB is a knowledgebase mainly dedicated to accumulate simulated events of this type. The main motivation behind LCG MCDB is to make the sophisticated MC event samples available for various physical groups. All the data from MCDB is accessible in several convenient ways. LCG MCDB is being developed within the CERN LCG Application Area Simulation project.

S. Belov; L. Dudko; E. Galkin; A. Gusev; W. Pokorski; A. Sherstnev

2007-03-27

473

Photon beam description in PEREGRINE for Monte Carlo dose calculations  

SciTech Connect

Goal of PEREGRINE is to provide capability for accurate, fast Monte Carlo calculation of radiation therapy dose distributions for routine clinical use and for research into efficacy of improved dose calculation. An accurate, efficient method of describing and sampling radiation sources is needed, and a simple, flexible solution is provided. The teletherapy source package for PEREGRINE, coupled with state-of-the-art Monte Carlo simulations of treatment heads, makes it possible to describe any teletherapy photon beam to the precision needed for highly accurate Monte Carlo dose calculations in complex clinical configurations that use standard patient modifiers such as collimator jaws, wedges, blocks, and/or multi-leaf collimators. Generic beam descriptions for a class of treatment machines can readily be adjusted to yield dose calculation to match specific clinical sites.

Cox, L. J., LLNL

1997-03-04

474

A Multivariate Time Series Method for Monte Carlo Reactor Analysis  

SciTech Connect

A robust multivariate time series method has been established for the Monte Carlo calculation of neutron multiplication problems. The method is termed Coarse Mesh Projection Method (CMPM) and can be implemented using the coarse statistical bins for acquisition of nuclear fission source data. A novel aspect of CMPM is the combination of the general technical principle of projection pursuit in the signal processing discipline and the neutron multiplication eigenvalue problem in the nuclear engineering discipline. CMPM enables reactor physicists to accurately evaluate major eigenvalue separations of nuclear reactors with continuous energy Monte Carlo calculation. CMPM was incorporated in the MCNP Monte Carlo particle transport code of Los Alamos National Laboratory. The great advantage of CMPM over the traditional Fission Matrix method is demonstrated for the three space-dimensional modeling of the initial core of a pressurized water reactor.

Taro Ueki

2008-08-14

475

Rapid Monte Carlo Simulation of Gravitational Wave Galaxies  

NASA Astrophysics Data System (ADS)

With the detection of gravitational waves on the horizon, astrophysical catalogs produced by gravitational wave observatories can be used to characterize the populations of sources and validate different galactic population models. Efforts to simulate gravitational wave catalogs and source populations generally focus on population synthesis models that require extensive time and computational power to produce a single simulated galaxy. Monte Carlo simulations of gravitational wave source populations can also be used to generate observation catalogs from the gravitational wave source population. Monte Carlo simulations have the advantes of flexibility and speed, enabling rapid galactic realizations as a function of galactic binary parameters with less time and compuational resources required. We present a Monte Carlo method for rapid galactic simulations of gravitational wave binary populations.

Breivik, Katelyn; Larson, Shane L.

2015-01-01

476

Monte Carlo studies of model Langmuir monolayers  

NASA Astrophysics Data System (ADS)

This paper examines some of the basic properties of a model Langmuir monolayer, consisting of surfactant molecules deposited onto a water subphase. The surfactants are modeled as rigid rods composed of a head and tail segment of diameters ?hh and ?tt, respectively. The tails consist of nt~4-7 effective monomers representing methylene groups. These rigid rods interact via site-site Lennard-Jones potentials with different interaction parameters for the tail-tail, head-tail, and head-head interactions. In a previous paper, we studied the ground-state properties of this system using a Landau approach. In the present paper, Monte Carlo simulations were performed in the canonical ensemble to elucidate the finite-temperature behavior of this system. Simulation techniques, incorporating a system of dynamic filters, allow us to decrease CPU time with negligible statistical error. This paper focuses on several of the key parameters, such as density, head-tail diameter mismatch, and chain length, responsible for driving transitions from uniformly tilted to untilted phases and between different tilt-ordered phases. Upon varying the density of the system, with ?hh=?tt, we observe a transition from a tilted (NNN)-condensed phase to an untilted-liquid phase and, upon comparison with recent experiments with fatty acid-alcohol and fatty acid-ester mixtures [M. C. Shih, M. K. Durbin, A. Malik, P. Zschack, and P. Dutta, J. Chem. Phys. 101, 9132 (1994); E. Teer, C. M. Knobler, C. Lautz, S. Wurlitzer, J. Kildae, and T. M. Fischer, J. Chem. Phys. 106, 1913 (1997)], we identify this as the L'2/Ov-L1 phase boundary. By varying the head-tail diameter ratio, we observe a decrease in Tc with increasing mismatch. However, as the chain length was increased we observed that the transition temperatures increased and differences in Tc due to head-tail diameter mismatch were diminished. In most of the present research, the water was treated as a hard surface, whereby the surfactants are only allowed to move within the plane of this surface. However, we have also utilized a more realistic model for the surfactant-water interactions, developed by Karaborni and Toxvaerd, in order to examine the role which the coupled effects of head group size and head group-subphase interactions plays in determining tilt ordering and on the stability of the monolayer. It is found that increasing the head diameter results in a widening of the air-water interface and an associated destruction of orientational order. Furthermore, the onset of capillary waves at lower temperatures for larger head diameters implies that the L2-L1 phase boundary for acids and acetates should move to lower temperatures relative to the L'2/Ov-L1 phase boundary for alcohols and esters. This feature has yet to be seen in experimental studies.

Opps, S. B.; Yang, B.; Gray, C. G.; Sullivan, D. E.

2001-04-01

477

Monte Carlo dose calculations in advanced radiotherapy  

NASA Astrophysics Data System (ADS)

The remarkable accuracy of Monte Carlo (MC) dose calculation algorithms has led to the widely accepted view that these methods should and will play a central role in the radiotherapy treatment verification and planning of the future. The advantages of using MC clinically are particularly evident for radiation fields passing through inhomogeneities, such as lung and air cavities, and for small fields, including those used in today's advanced intensity modulated radiotherapy techniques. Many investigators have reported significant dosimetric differences between MC and conventional dose calculations in such complex situations, and have demonstrated experimentally the unmatched ability of MC calculations in modeling charged particle disequilibrium. The advantages of using MC dose calculations do come at a cost. The nature of MC dose calculations require a highly detailed, in-depth representation of the physical system (accelerator head geometry/composition, anatomical patient geometry/composition and particle interaction physics) to allow accurate modeling of external beam radiation therapy treatments. To perform such simulations is computationally demanding and has only recently become feasible within mainstream radiotherapy practices. In addition, the output of the accelerator head simulation can be highly sensitive to inaccuracies within a model that may not be known with sufficient detail. The goal of this dissertation is to both improve and advance the implementation of MC dose calculations in modern external beam radiotherapy. To begin, a novel method is proposed to fine-tune the output of an accelerator model to better represent the measured output. In this method an intensity distribution of the electron beam incident on the model is inferred by employing a simulated annealing algorithm. The method allows an investigation of arbitrary electron beam intensity distributions and is not restricted to the commonly assumed Gaussian intensity. In a second component of this dissertation the design, implementation and evaluation of a technique for reducing a latent variance inherent from the recycling of phase space particle tracks in a simulation is presented. In the technique a random azimuthal rotation about the beam's central axis is applied to each recycled particle, achieving a significant reduction of the latent variance. In a third component, the dissertation presents the first MC modeling of Varian's new RapidArc delivery system and a comparison of dose calculations with the Eclipse treatment planning system. A total of four arc plans are compared including an oropharynx patient phantom containing tissue inhomogeneities. Finally, in a step toward introducing MC dose calculation into the planning of treatments such as RapidArc, a technique is presented to feasibly generate and store a large set of MC calculated dose distributions. A novel 3-D dyadic multi-resolution (MR) decomposition algorithm is presented and the compressibility of the dose data using this algorithm is investigated. The presented MC beamlet generation method, in conjunction with the presented 3-D data MR decomposition, represents a viable means to introduce MC dose calculation in the planning and optimization stages of advanced radiotherapy.

Bush, Karl Kenneth

478

Bold Diagrammatic Monte Carlo Study of $\\phi^4$ Theory  

E-print Network

By incorporating renormalization procedure into Bold Diagrammatic Monte Carlo (BDMC), we propose a method for studying quantum field theories in the strong coupling regime. BDMC essentially samples Feynman diagrams using local Metropolis-type updates and does not suffer from the sign problem. Applying the method to three dimensional $\\phi^4$ theory, we analyze the strong coupling limit of the theory and confirm the existence of a nontrivial IR fixed point in agreement with prior studies. Interestingly, we find that working with bold correlation functions as building blocks of the Monte Carlo procedure, renders the scheme convergent and no further resummation method is needed.

Davody, Ali

2013-01-01

479

Precise Monte Carlo Simulation of Single-Photon Detectors  

E-print Network

We demonstrate the importance and utility of Monte Carlo simulation of single-photon detectors. Devising an optimal simulation is strongly influenced by the particular application because of the complexity of modern, avalanche-diode-based single-photon detectors.. Using a simple yet very demanding example of random number generation via detection of Poissonian photons exiting a beam splitter, we present a Monte Carlo simulation that faithfully reproduces the serial autocorrelation of random bits as a function of detection frequency over four orders of magnitude of the incident photon flux. We conjecture that this simulation approach can be easily modified for use in many other applications.

Mario Stip?evi?; Daniel J. Gauthier

2014-11-13

480

Quantum Monte Carlo calculations of BiFeO3  

NASA Astrophysics Data System (ADS)

Multiferroic Bismuth Ferrite (BiFeO3) exhibits both ferroelectricity and antiferromagnetism, possibly enabling a connection between the two effects in the same material. While its antiferromagnetic character is relatively well-understood, experimental measurements of the spontaneous polarization vary significantly over two orders of magnitude, from 0.06 C/m^2 to 1.50 C/m^2. We cary out accurate quantum Monte Carlo calculations to estimate the cohesion energy and the ferroelectric distortion well depth. We discuss the mechanisms proposed to understand the variations of polarization experimental data in the light of our quantum Monte Carlo results.

Wagner, Lucas K.; Sulock, David; Mitas, Lubos

2007-03-01

481

Collective translational and rotational Monte Carlo moves for attractive particles.  

PubMed

Virtual move Monte Carlo is a Monte Carlo (MC) cluster algorithm forming clusters via local energy gradients and approximating the collective kinetic or dynamic motion of attractive colloidal particles. We carefully describe, analyze, and test the algorithm. To formally validate the algorithm through highlighting its symmetries, we present alternative and compact ways of selecting and accepting clusters which illustrate the formal use of abstract concepts in the design of biased MC techniques: the superdetailed balance and the early rejection scheme. A brief and comprehensive summary of the algorithms is presented, which makes them accessible without needing to understand the details of the derivation. PMID:24730967

R?ži?ka, Št?pán; Allen, Michael P

2014-03-01

482

Monte Carlo simulation of lattice systems with RKKY interaction  

NASA Astrophysics Data System (ADS)

Numerical approaches to the study of the magnetic states, properties, and phase transitions in the Ising spin systems with the long-range exchange interaction is presented. The Monte Carlo calculations have been performed for a system of Ising spins on a square lattice with long-range RKKY interaction. It is shown that the Monte Carlo simulation systems RKKY interaction leads to the formation of a complex of the magnetic structure. We compared the results of simulation with experimental images of domain structure of garnet ferrite films.

Nefedev, K. V.; Belokon, V. I.; Kapitan, V. Yu; Dyachenko, O. I.

2014-03-01

483

Monte Carlo Method for a Quantum Measurement Process by a Single-Electron Transistor  

E-print Network

We derive the quantum trajectory or stochastic (conditional) master equation for a single superconducting Cooper-pair box (SCB) charge qubit measured by a single-electron transistor (SET) detector. This stochastic master equation describes the random evolution of the measured SCB qubit density matrix which both conditions and is conditioned on a particular realization of the measured electron tunneling events through the SET junctions. Hence it can be regarded as a Monte Carlo method that allows us to simulate the continuous quantum measurement process. We show that the master equation for the "partially" reduced density matrix [Y. Makhlin et.al., Phys. Rev. Lett. 85, 4578 (2000)] can be obtained when a "partial" average is taken on the stochastic master equation over the fine grained measurement records of the tunneling events in the SET. Finally, we present some Monte Carlo simulation results for the SCB/SET measurement process. We also analyze the probability distribution P(m,t) of finding m electrons that have tunneled into the drain of the SET in time t to demonstrate the connection between the quantum trajectory approach and the "partially" reduced density matrix approach.

Hsi-Sheng Goan

2004-06-15

484

Quantum Monte Carlo Computations for Minerals at High Pressures  

NASA Astrophysics Data System (ADS)

We have performed Quantum Monte Carlo (QMC) computations for silica, FeO, and c-BN as functions of compression. QMC uses no approximate density functional, and the many-body, correlated, Schrödinger equation is effectively solved stochastically. In spite of the great success of DFT there are still some fundamental problems that need improvement. First is the need for increased accuracy for some rather ordinary materials such as silica. Although the local density approximation (LDA) gives excellent results for individual silica phases, such as the CaCl2 transition, it is not so good for comparing energetics of very different structures, such as quartz versus stishovite. Our QMC results will be used to improve density functionals, and show the way towards more accurate computations for Earth materials. Thermal contributions are included using density functional perturbation theory with the code ABINIT. We have computed the shear elastic constant c11-c12 in stishovite, which is associated with the phase transition to the CaCl2 structure, with QMC. We are developing a first-principles high-pressure standard using cubic BN. For this we are performing the first all-electron QMC computations for solids with atoms heavier than He. We are also performing QMC computations on FeO to understand better the importance and nature of magnetism in FeO under pressure. This work is supported by NSF grants EAR-0530282, EAR- 0310139, and by DOE contract DE-FG02-99ER45795 to John Wilkins. Computations were performed on blueice at NCAR under a BTS grant, and on tungsten and abe at NCSA, and at the Carnegie Institution of Washington.

Cohen, R. E.; Esler, K.; Shulenburger, L.; Driver, K.; Wu, Z.; Militzer, B.; Towler, M.; Needs, R.

2008-12-01

485

Analysis and Monte Carlo simulation of near-terminal aircraft flight paths  

NASA Technical Reports Server (NTRS)

The flight paths of arriving and departing aircraft at an airport are stochastically represented. Radar data of the aircraft movements are used to decompose the flight paths into linear and curvilinear segments. Variables which describe the segments are derived, and the best fitting probability distributions of the variables, based on a sample of flight paths, are found. Conversely, given information on the probability distribution of the variables, generation of a random sample of flight paths in a Monte Carlo simulation is discussed. Actual flight paths at Dulles International Airport are analyzed and simulated.

Schiess, J. R.; Matthews, C. G.

1982-01-01

486

Monte Carlo simulation-based approach to model the size distribution of metastatic tumors  

NASA Astrophysics Data System (ADS)

The size distribution of metastatic tumors and its time evolution are traditionally described by integrodifferential equations and stochastic models. Here we develop a simple Monte Carlo approach in which each event of metastasis is treated as a chance event through random-number generation. We demonstrate the accuracy of this approach on a specific growth and metastasis model by showing that it quantitatively reproduces the size distribution and the total number of tumors as a function of time. The approach also yields statistical distribution of patient-to-patient variations, and has the flexibility to incorporate many real-life complexities.

Maiti, Esha

2012-01-01

487

A Constrained-Path Quantum Monte-Carlo Approach for the Nuclear Shell Model  

E-print Network

A new Quantum Monte-Carlo (QMC) approach is proposed to investigate low-lying states of nuclei within the shell model. The formalism relies on a variational symmetry-restored wave-function to guide the underlying Brownian motion. Sign/phase problems that usually plague QMC fermionic simulations are controlled by constraining stochastic paths through a fixed-node like approximation. Exploratory results in the sd and pf valence spaces with realistic effective interactions are presented. They prove the ability of the scheme to yield nearly exact yrast spectroscopies for both even- and odd-mass nuclei.

Jérémy Bonnard; Olivier Juillet

2013-03-27

488

Blind Data Detection in the Presence of PLL Phase Noise by Sequential Monte Carlo Method  

E-print Network

Blind Data Detection in the Presence of PLL Phase Noise by Sequential Monte Carlo Method Erdal Abstract-- In this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm

Noels, Nele

489

Monte Carlo f calculation of the neoclassical ion current in a rotating island  

E-print Network

Monte Carlo f calculation of the neoclassical ion current in a rotating island A. Bergmann, E. Poli is considered. We use a guiding centre f code augmented by a Monte Carlo model of pitch angle collisions

490

APPLICATION OF BAYESIAN MONTE CARLO ANALYSIS TO A LAGRANGIAN PHOTOCHEMICAL AIR QUALITY MODEL. (R824792)  

EPA Science Inventory

Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...

491

Monte Carlo Simulations of Thermal Conductivity in Nanoporous Si Membranes  

E-print Network

1 Monte Carlo Simulations of Thermal Conductivity in Nanoporous Si Membranes Stefanie Wolf1 University of Vienna, Austria 2 School of Engineering, University of Warwick, Coventry, CV4 7AL, UK {wolf: Dispersion relations for longitudinal acoustic (LA) phonons in red-solid, and transversal acoustic (TA

492

A Markov chain Monte Carlo analysis of the CMSSM  

Microsoft Academic Search

We perform a comprehensive exploration of the Constrained MSSM parameter space employing a Markov Chain Monte Carlo technique and a Bayesian analysis. We compute superpartner masses and other collider observables, as well as a cold dark matter abundance, and compare them with experimental data. We include uncertainties arising from theoretical approximations as well as from residual experimental errors of relevant

Roberto Ruiz de Austri; Roberto Trotta; Leszek Roszkowski

2006-01-01

493

Microbial contamination in poultry chillers estimated by Monte Carlo simulations  

Technology Transfer Automated Retrieval System (TEKTRAN)

The risk of microbial contamination during poultry processing may be reduced by the operating characteristics of the chiller. The performance of air chillers and immersion chillers were compared in terms of pre-chill and post-chill contamination using Monte Carlo simulations. Three parameters were u...

494

Monte Carlo Simulations of Light Propagation in Apples  

Technology Transfer Automated Retrieval System (TEKTRAN)

This paper reports on the investigation of light propagation in fresh apples in the visible and short-wave near-infrared region using Monte Carlo simulations. Optical properties of ‘Golden Delicious’ apples were determined over the spectral range of 500-1100 nm using a hyperspectral imaging method, ...

495

A Monte Carlo Approach for Adaptive Testing with Content Constraints  

ERIC Educational Resources Information Center

This article presents a new algorithm for computerized adaptive testing (CAT) when content constraints are present. The algorithm is based on shadow CAT methodology to meet content constraints but applies Monte Carlo methods and provides the following advantages over shadow CAT: (a) lower maximum item exposure rates, (b) higher utilization of the…

Belov, Dmitry I.; Armstrong, Ronald D.; Weissman, Alexander

2008-01-01

496

Exploring Mass Perception with Markov Chain Monte Carlo  

ERIC Educational Resources Information Center

Several previous studies have examined the ability to judge the relative mass of objects in idealized collisions. With a newly developed technique of psychological Markov chain Monte Carlo sampling (A. N. Sanborn & T. L. Griffiths, 2008), this work explores participants; perceptions of different collision mass ratios. The results reveal…

Cohen, Andrew L.; Ross, Michael G.

2009-01-01

497

A Variational Monte Carlo Approach to Atomic Structure  

ERIC Educational Resources Information Center

The practicality and usefulness of variational Monte Carlo calculations to atomic structure are demonstrated. It is found to succeed in quantitatively illustrating electron shielding, effective nuclear charge, l-dependence of the orbital energies, and singlet-tripetenergy splitting and ionization energy trends in atomic structure theory.

Davis, Stephen L.

2007-01-01

498

Monte Carlo Sampling for Regret Minimization in Extensive Games  

E-print Network

Monte Carlo Sampling for Regret Minimization in Extensive Games Marc Lanctot Department of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 lanctot@ualberta.ca Kevin Waugh of Computing Science University of Alberta Edmonton, Alberta, Canada T6G 2E8 bowling@cs.ualberta.ca Abstract

Bowling, Michael

499

Supplementary Material for Monte Carlo Bayesian Reinforcement Learning  

E-print Network

Supplementary Material for Monte Carlo Bayesian Reinforcement Learning 1. Proof of Theorem 1 Vi = E (V (i, )). On the other hand, ^Vi is the value of i for the discrete POMDP ^P, which's inequality gives p ^Vi - Vi i = p 1 K K k=1 V (i, ^k ) - E (V (i, )) i exp - K 2 i 2C2 , (1) where C

Lee, Wee Sun

500

High Performance Monte-Carlo Based Option Pricing on FPGAs  

Microsoft Academic Search

High performance computing is becoming increasingly important in the field of financial computing, as the complexity of financial models continues to increase. Many of these financial models do not have a practical close form solution in which case numerical methods are the only alternative. Monte-Carlo simulation is one of most commonly used numerical methods, in scientific computing in general, with

Xiang Tian; Khaled Benkrid; Xiaochen Gu

2008-01-01