Absorbing Markov Chain solution for Possion's equation

Keming Gu, M. Sadiku
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引用次数: 7

Abstract

The Markov chain method for solving Laplace's equation with Dirichlet boundary condition has been discussed in a few papers. This paper presents an absorbing Markov chain method to solve Possion's equation with Dirichlet boundary condition. In the Markov chain, the fundamental matrix N defines the transient relationships for a randomly walking particle from state s/sub j/ passing through state s/sub i/ before it reaches the absorbing state. From the fundamental matrix N and the probability matrix R from non-absorbing states to absorbing states, the contributions of boundary points and interior points to the potential of internal points are defined. The absorbing Markov chain method overcomes a major disadvantage of classic Monte Carlo methods that they are only capable of calculating the potential at a single point at a time unlike other numerical methods such as finite difference and finite element methods which provide simultaneously the solution at all the grid nodes. This paper presents an example to show the accuracy of the absorbing Markov chains solution.
Possion方程的吸收马尔可夫链解
本文讨论了求解具有Dirichlet边界条件的拉普拉斯方程的马尔可夫链法。本文提出了一种吸收马尔可夫链法求解具有Dirichlet边界条件的Possion方程。在马尔可夫链中,基本矩阵N定义了随机行走粒子从状态s/sub j/经过状态s/sub i/到达吸收态之前的瞬态关系。从基本矩阵N和从非吸收态到吸收态的概率矩阵R出发,定义了边界点和内部点对内部点势的贡献。吸收马尔可夫链方法克服了经典蒙特卡罗方法一次只能计算单个点的电势的主要缺点,而其他数值方法如有限差分法和有限元法则同时提供所有网格节点的解。本文通过一个算例说明了吸收马尔可夫链解的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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