Topic
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.
Papers published on a yearly basis
Papers
More filters
01 Jan 1992
TL;DR: In this paper, the authors propose a method to solve the problem of the problem: this paper... ]..,.. )].. [1].
Abstract: ii
269 citations
14 May 1963
TL;DR: The purpose of this paper is to try to charac terize the qualitative aspects of individuals networks which indicate whether the reduction of a stochastic longest path problem to a de terministic one is adequate and to some extent, to estimate the magnitude of the errors involved.
Abstract: : The purpose of this paper is to try to charac terize the qualitative aspectsdividual networks which indicate whether the reduction of a stochastic longest path problem to a de terministic one is adequate and to some extent, to estimate the magnitude of the errors involved.
269 citations
01 Jan 2014
TL;DR: In this article, a Monte Carlo simulation is used to evaluate the physical quantities related to the interaction of electrons with a solid target, and the cross-sections and mean free paths have to be previously accurately calculated: they are then used in the Monte Carlo code in order to obtain the macroscopic characteristics of the interaction processes.
Abstract: Monte Carlo is one of the most powerful theoretical methods for evaluating the physical quantities related to the interaction of electrons with a solid target. A Monte Carlo simulation can be considered as an idealized experiment. The simulation does not investigate the fundamental principles of the interaction. It is necessary to have a good knowledge of them – in particular of the energy loss and angular deflection phenomena – to produce a good simulation. All the cross-sections and mean free paths have to be previously accurately calculated: they are then used in the Monte Carlo code in order to obtain the macroscopic characteristics of the interaction processes by simulating a large number of single particle trajectories and then averaging them. Due to the recent evolution in computer calculation capability, we are now able to obtain statistically significant results in very short calculation times.
268 citations
TL;DR: In this article, a Monte Carlo method for obtaining solutions of the Boltzmann equation to describe phonon transport in micro-and nanoscale devices is presented, which can resolve arbitrarily small signals at small constant cost and thus represents a considerable improvement compared to traditional Monte Carlo methods, whose cost increases quadratically with decreasing signal.
Abstract: We present a Monte Carlo method for obtaining solutions of the Boltzmann equation to describe phonon transport in micro- and nanoscale devices. The proposed method can resolve arbitrarily small signals (e.g., temperature differences) at small constant cost and thus represents a considerable improvement compared to traditional Monte Carlo methods, whose cost increases quadratically with decreasing signal. This is achieved via a control-variate variance-reduction formulation in which the stochastic particle description solves only for the deviation from a nearby equilibrium, while the latter is described analytically. We also show that simulation of an energy-based Boltzmann equation results in an algorithm that lends itself naturally to exact energy conservation, thereby considerably improving the simulation fidelity. Simulations using the proposed method are used to investigate the effect of porosity on the effective thermal conductivity of silicon. We also present simulations of a recently developed thermal conductivity spectroscopy process. The latter simulations demonstrate how the computational gains introduced by the proposed method enable the simulation of otherwise intractable multiscale phenomena.
268 citations
TL;DR: In this paper, the multicanonical approach is not restricted to Monte Carlo simulations, but can also be applied to simulation techniques such as the molecular dynamics, Langevin and hybrid Monte Carlo algorithms.
268 citations