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Dynamic Monte Carlo method
About: Dynamic Monte Carlo method is a research topic. Over the lifetime, 13294 publications have been published within this topic receiving 371256 citations.
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TL;DR: In this paper, Monte Carlo programs for lattice models using supercomputers are vectorized using Monte Carlo methods for improved efficiency of computer simulations in statistical mechanics, including simulation of random growth processes.
Abstract: Vectorisation of Monte Carlo programs for lattice models using supercomputers.- Parallel algorithms for statistical physics problems.- New monte carlo methods for improved efficiency of computer simulations in statistical mechanics.- Simulation of random growth processes.- Recent progress in the simulation of classical fluids.- Monte Carlo techniques for quantum fluids, solids and droplets.- Quantum lattice problems.- Simulations of macromolecules.- Percolation, critical phenomena in dilute magnets, cellular automata and related problems.- Interfaces, wetting phenomena, incommensurate phases.- Spin glasses, orientational glasses and random field systems.- Recent developments in the Monte Carlo simulation of condensed matter.
320 citations
01 Jun 2003-Nuclear Instruments & Methods in Physics Research Section B-beam Interactions With Materials and Atoms
TL;DR: In this paper, the authors discuss the mixed (class II) scheme used to transport intermediate-and high-energy electrons and positrons and, in particular, the approximations adopted to account for the energy dependence of the interaction cross-sections.
Abstract: The physical algorithms implemented in the latest release of the general-purpose Monte Carlo code penelope for the simulation of coupled electron–photon transport are briefly described. We discuss the mixed (class II) scheme used to transport intermediate- and high-energy electrons and positrons and, in particular, the approximations adopted to account for the energy dependence of the interaction cross-sections. The reliability of the simulation code, i.e. of the adopted interaction models and tracking algorithms, is analyzed by means of a comprehensive comparison of simulation results with experimental data available from the literature. The present analysis demonstrates that penelope yields a consistent description of electron transport processes in the energy range from a few keV up to about 1 GeV.
319 citations
TL;DR: It is shown how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.
Abstract: Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.
318 citations
317 citations
TL;DR: In this paper, Monte Carlo sampling is used for nonlinear inverse problems where no analytical expression for the forward relation between data and model parameters is available, and where linearization is unsuccessful.
Abstract: 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 Carlo methods can be divided into two categories: the sampling methods and the optimization methods. Monte Carlo sampling is useful when the space of feasible solutions is to be explored, and measures of resolution and uncertainty of solution are needed. The Metropolis algorithm and the Gibbs sampler are the most widely used Monte Carlo samplers for this purpose, but these methods can be refined and supplemented in various ways of which the neighbourhood algorithm is a notable example. Monte Carlo optimization methods are powerful tools when searching for globally optimal solutions amongst numerous local optima. Simulated annealing and genetic algorithms have shown their strength in this respect, but they suffer from the same fundamental problem as the Monte Carlo sampling methods: no provably optimal strategy for tuning these methods to a given problem has been found, only a number of approximate methods.
311 citations