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Random Crank-Nicolson Scheme for Random Heat Equation in Mean Square Sense

机译:均方意义上的随机热方程的随机Crank-Nicolson方案

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The goal of computational science is to develop models that predict phenomena observed in nature. However, these models are often based on parameters that are uncertain. In recent decades, main numerical methods for solving SPDEs have been used such as, finite difference and finite element schemes [1]-[5]. Also, some practical techniques like the method of lines for boundary value problems have been applied to the linear stochastic partial differential equations, and the outcomes of these approaches have been experimented numerically [7]. In [8]-[10], the author discussed mean square convergent finite difference method for solving some random partial differential equations. Random numerical techniques for both ordinary and partial random differential equations are treated in [4] [10]. As regards applications using explicit analytic solutions or numerical methods, a few results may be found in [5] [6] [11]. This article focuses on solving random heat equation by using Crank-Nicol- son technique under mean square sense and it is organized as follows. In Section 2, the mean square calculus preliminaries that will be required throughout the paper are presented. In Section 3, the Crank-Nicolson scheme for solving the random heat equation is presented. In Section 4, some case studies are showed. Short conclusions are cleared in the end section.
机译:计算科学的目标是开发可预测自然界中观察到的现象的模型。但是,这些模型通常基于不确定的参数。近几十年来,已经使用了求解SPDE的主要数值方法,例如有限差分法和有限元方案[1]-[5]。另外,一些实用技术,例如用于边值问题的线法,已经应用于线性随机偏微分方程,并且对这些方法的结果进行了数值实验[7]。在[8]-[10]中,作者讨论了求解某些随机偏微分方程的均方收敛有限差分方法。 [4] [10]中讨论了用于普通和偏微分方程的随机数值技术。至于使用显式解析解或数值方法的应用,可能会在[5] [6] [11]中找到一些结果。本文致力于在均方意义下使用Crank-Nicolson技术求解随机热方程,其结构如下。在第2节中,将介绍整个论文中所需要的均方根演算的初步知识。在第3节中,提出了解决随机热方程的Crank-Nicolson方案。在第4节中,显示了一些案例研究。简短的结论在最后一节中得到了澄清。

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