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arXiv 1702.01347math.NAcs.NAmath.PR
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可计算的后验界用于随机Allen-Cahn方程

Numerically Computable A Posteriori-Bounds for stochastic Allen-Cahn equation

Institut für Mathematik Universität Augsburg(奥格斯堡大学数学研究所);Department of Mathematics, Faculty of Sciences, Razi University(拉兹理学院数学系)

Dirk Blömker, Minoo Kamrani

AI总结:

本文基于指数时间方案和谱 Galerkin 方法,推导了随机Allen-Cahn方程数值方法的后验误差估计,通过计算数据获得严格且可计算的均方误差界,适用于未知解全局存在的方程。

摘要:

本文旨在推导基于指数时间方案和谱 Galerkin 方法的数值方法的后验误差估计。我们通过计算数据严格地获得了均方误差的界,该界可计算且利用给定的数值近似。因此,可以不依赖渐近结果来检查给定数值计算的误差。所有估计仅基于数值数据和方程的结构,但不使用解的先验信息,这使该方法适用于解的全局存在性未知的方程。为简化展示,本文在相对简单的稳定一维Allen-Cahn方程加加性强迫的情况下开发了该方法。

英文摘要:

The aim of this paper is the derivation of an a-posteriori error estimate for a numerical method based on an exponential scheme in time and spectral Galerkin methods in space. We obtain analytically a rigorous bound on the mean square error conditioned to the calculated data, which is numerically computable and uses the given numerical approximation. Thus one can check a-posteriori the error for a given numerical computation without relying on an asymptotic result. All estimates are only based on the numerical data and the structure of the equation, but they do not use any a-priori information of the solution, which makes the approach applicable to equations where global existence of solutions is not known. For simplicity of presentation, we develop the method here in a relatively simple situation of a stable one-dimensional Allen-Cahn equation with additive forcing.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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