有限空间域中热方程的完全离散Schwarz波形松弛分析

IF 1.9 3区 数学 Q2 Mathematics
Ronald D. Haynes, Khaled Mohammad
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引用次数: 2

摘要

施瓦兹波形松弛方法为时变偏微分方程的解提供了时空并行性。这些算法通过在引入的时空边界上强制传输条件的选择来区分。早期的结果考虑了这些算法在连续和半离散(在空间)设置的各种族线性偏微分方程的理论分析。然后在无限空间域的简化假设下得到了完全离散的结果。本文首次分析了在任意有限数量有界子域上求解一维热方程的完全离散经典Schwarz波形算法。选择θ -方法作为时间积分器。给出了在无穷范数和二范数下的收敛结果,并给出了在一致分划情况下的显式收缩。将计算结果与数值计算结果和先前的理论结果进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully discrete Schwarz waveform relaxation analysis for the heat equation on a finite spatial domain
Schwarz waveform relaxation methods provide space-time parallelism for the solution of time dependent partial differential equations. The algorithms are differentiated by the choice of the transmission conditions enforced at the introduced space-time boundaries. Early results considered the theoretical analysis of these algorithms in the continuous and semi-discrete (in space) settings for various families of linear partial differential equations. Later, fully discrete results were obtained under the simplifying assumption of an infinite spatial domain. In this paper, we provide a first analysis of a fully discrete classical Schwarz Waveform algorithm for the one–dimensional heat equation on an arbitrary but finite number of bounded subdomains. The θ –method is chosen as the time integrator. Convergence results are given in both the infinity norm and two norm, with an explicit contraction given in the case of a uniform partitioning. The results are compared to the numerics and to the earlier theoretical results.
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来源期刊
CiteScore
2.70
自引率
5.30%
发文量
27
审稿时长
6-12 weeks
期刊介绍: M2AN publishes original research papers of high scientific quality in two areas: Mathematical Modelling, and Numerical Analysis. Mathematical Modelling comprises the development and study of a mathematical formulation of a problem. Numerical Analysis comprises the formulation and study of a numerical approximation or solution approach to a mathematically formulated problem. Papers should be of interest to researchers and practitioners that value both rigorous theoretical analysis and solid evidence of computational relevance.
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