谱ACMS:一种鲁棒局部逼近分量模态综合方法

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Alexandre L. Madureira, Marcus Sarkis
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引用次数: 0

摘要

SIAM数值分析杂志,第63卷,第3期,1055-1077页,2025年6月。摘要。本文考虑多尺度型有限元方法来近似求解具有非均匀系数的二维对称椭圆型偏微分方程。该方法是Galerkin型的,遵循变分多尺度和局部正交分解(LOD)方法,将空间解耦为多尺度和精细子空间。第一种方法是基于原始迭代子结构方法中使用的角点,将粗糙基函数映射到全局最小能量函数,从而得到多尺度基函数。这种方法相对于网格大小提供了准最优的先验误差能量近似,但相对于高对比度系数,它不是鲁棒的。第二种方法是将基于局部广义特征值问题的边模加入到角模中。结果,获得了与网格和对比度无关的最优先验误差能量估计。即使解具有最小正则性(只属于Sobolev空间[math]),这些方法也能以最优速率收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral ACMS: A Robust Localized Approximated Component Mode Synthesis Method
SIAM Journal on Numerical Analysis, Volume 63, Issue 3, Page 1055-1077, June 2025.
Abstract. We consider finite element methods of multiscale type to approximate solutions for two-dimensional symmetric elliptic partial differential equations with heterogeneous [math] coefficients. The methods are of Galerkin type and follow the Variational Multiscale and Localized Orthogonal Decomposition (LOD) approaches in the sense that it decouples spaces into multiscale and fine subspaces. In a first method, the multiscale basis functions are obtained by mapping coarse basis functions, based on corners used on primal iterative substructuring methods, to functions of global minimal energy. This approach delivers quasi-optimal a priori error energy approximation with respect to the mesh size, but it is not robust with respect to high-contrast coefficients. In a second method, edge modes based on local generalized eigenvalue problems are added to the corner modes. As a result, optimal a priori error energy estimate is achieved which is mesh and contrast independent. The methods converge at optimal rate even if the solution has minimum regularity, belonging only to the Sobolev space [math].
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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