A greedy MOR method for the tracking of eigensolutions to parametric elliptic PDEs

IF 2.6 2区 数学 Q1 MATHEMATICS, APPLIED Journal of Computational and Applied Mathematics Pub Date : 2025-03-15 Epub Date: 2024-09-17 DOI:10.1016/j.cam.2024.116270
Moataz Alghamdi , Daniele Boffi , Francesca Bonizzoni
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Abstract

In this paper we introduce a Model Order Reduction (MOR) algorithm based on a sparse grid adaptive refinement, for the approximation of the eigensolutions to parametric problems arising from elliptic partial differential equations. In particular, we are interested in detecting the crossing of the hypersurfaces describing the eigenvalues as a function of the parameters.
The a priori matching is followed by an a posteriori verification, driven by a suitably defined error indicator. At a given refinement level, a sparse grid approach is adopted for the construction of the grid of the next level, by using the marking given by the a posteriori indicator.
Various numerical tests confirm the good performance of the scheme.
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用于跟踪参数椭圆 PDEs 特征解的贪婪 MOR 方法
本文介绍了一种基于稀疏网格自适应细化的模型阶次缩减(MOR)算法,用于逼近椭圆偏微分方程参数问题的特征值解。特别是,我们感兴趣的是检测描述特征值的超曲面与参数函数的交叉。先验匹配之后是后验验证,由适当定义的误差指标驱动。在给定的细化级别上,通过使用后验指标给出的标记,采用稀疏网格方法构建下一级别的网格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.40
自引率
4.20%
发文量
437
审稿时长
3.0 months
期刊介绍: The Journal of Computational and Applied Mathematics publishes original papers of high scientific value in all areas of computational and applied mathematics. The main interest of the Journal is in papers that describe and analyze new computational techniques for solving scientific or engineering problems. Also the improved analysis, including the effectiveness and applicability, of existing methods and algorithms is of importance. The computational efficiency (e.g. the convergence, stability, accuracy, ...) should be proved and illustrated by nontrivial numerical examples. Papers describing only variants of existing methods, without adding significant new computational properties are not of interest. The audience consists of: applied mathematicians, numerical analysts, computational scientists and engineers.
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