环形椭圆BVP的局部MFS矩阵分解算法

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-02-01 DOI:10.4208/nmtma.oa-2023-0045

C.S. Chen,Andreas Karageorghis, Min Lei

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引用次数: 0

摘要

我们将基本解局部法（LMFS）应用于拉普拉斯方程和同质双谐波方程的边界值问题（BVPs）。通过适当选择配位点，LMFS离散化得到稀疏的分块环形系统矩阵。因此，可以使用矩阵分解算法（MDA）和快速傅立叶变换（FFT）来求解系统，从而大大节省了计算时间和存储需求。通过将该方法应用于几个数值实验，证明了该方法的准确性及其解决大型问题的能力。

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Local MFS Matrix Decomposition Algorithms for Elliptic BVPs in Annuli

We apply the local method of fundamental solutions (LMFS) to boundary value problems (BVPs) for the Laplace and homogeneous biharmonic equations in annuli. By appropriately choosing the collocation points, the LMFS discretization yields sparse block circulant system matrices. As a result, matrix decomposition algorithms (MDAs) and fast Fourier transforms (FFTs) can be used for the solution of the systems resulting in considerable savings in both computational time and storage requirements. The accuracy of the method and its ability to solve large scale problems are demonstrated by applying it to several numerical experiments.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.