异质介质中时间分数次扩散和扩散波方程的迪里夏-诺伊曼波形松弛算法收敛性分析

IF 1.7 3区 数学 Q2 MATHEMATICS, APPLIED
Soura Sana, Bankim C Mandal
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引用次数: 0

摘要

本文全面研究了迪里赫特-诺伊曼波形松弛算法的收敛行为,该算法适用于求解多子域中的时间分数子扩散方程和扩散波方程,并考虑了一些异质介质的存在。我们的分析重点是估算算法的收敛率,并研究这一估算值如何随不同分数阶而变化。此外,我们还将分析扩展到二维子扩散情况。为了验证我们的研究结果,我们进行了数值实验来验证估计的收敛速率。结果证实了理论估计,并为算法的效率和可靠性提供了经验证据。此外,我们还将该算法扩展到解决时间分数 Allen-Chan 方程,从而拓展了算法的适用范围,这个问题超出了我们最初的理论估计。值得注意的是,我们发现该算法在这种扩展情况下,无论是短时间窗口还是长时间窗口,都表现得异常出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence analysis of the Dirichlet-Neumann Waveform Relaxation algorithm for time fractional sub-diffusion and diffusion-wave equations in heterogeneous media

This article presents a comprehensive study on the convergence behavior of the Dirichlet-Neumann Waveform Relaxation algorithm applied to solve the time fractional sub-diffusion and diffusion-wave equations in multiple subdomains, considering the presence of some heterogeneous media. Our analysis focuses on estimating the convergence rate of the algorithm and investigates how this estimate varies with different fractional orders. Furthermore, we extend our analysis to encompass the 2D sub-diffusion case. To validate our findings, we conduct numerical experiments to verify the estimated convergence rate. The results confirm the theoretical estimates and provide empirical evidence for the algorithm’s efficiency and reliability. Moreover, we push the boundaries of the algorithm’s applicability by extending it to solve the time fractional Allen-Chan equation, a problem that exceeds our initial theoretical estimates. Remarkably, we observe that the algorithm performs exceptionally well in this extended scenario for both short and long-time windows.

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来源期刊
CiteScore
3.00
自引率
5.90%
发文量
68
审稿时长
3 months
期刊介绍: Advances in Computational Mathematics publishes high quality, accessible and original articles at the forefront of computational and applied mathematics, with a clear potential for impact across the sciences. The journal emphasizes three core areas: approximation theory and computational geometry; numerical analysis, modelling and simulation; imaging, signal processing and data analysis. This journal welcomes papers that are accessible to a broad audience in the mathematical sciences and that show either an advance in computational methodology or a novel scientific application area, or both. Methods papers should rely on rigorous analysis and/or convincing numerical studies.
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