arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2211.02484math.NAcs.NA
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一种改进的椭圆多尺度问题高阶方法

An improved high-order method for elliptic multiscale problems

Zhaonan Dong, Moritz Hauck, Roland Maier

AI总结:

本文提出一种改进的高阶多尺度方法,通过改进局部化策略克服局部子域不足时的误差恶化问题,仅依赖右端项正则性即可获得高阶收敛率。

摘要:

本文针对具有粗糙且可能高度振荡系数的椭圆模型问题,提出了一种高阶多尺度方法。仅利用右端项的正则性即可获得高阶收敛率,因此无需对系数、区域或精确解施加限制性假设。秉承局部正交分解(Localized Orthogonal Decomposition)的思想,该方法通过在局部子域上求解辅助问题来构造与粗问题适配的试探空间。更确切地说,我们的方法基于 Maier [SIAM J. Numer. Anal. 59(2), 2021] 提出的策略。所提方法的独特优势在于一种改进的局部化策略,当局部子域不够大时,该策略能够消除误差随网格尺寸增大而恶化的效应。我们给出了严格的先验误差分析,并通过一系列数值实验验证了该方法的性能。

英文摘要:

In this work, we propose a high-order multiscale method for an elliptic model problem with rough and possibly highly oscillatory coefficients. Convergence rates of higher order are obtained using the regularity of the right-hand side only. Hence, no restrictive assumptions on the coefficient, the domain, or the exact solution are required. In the spirit of the Localized Orthogonal Decomposition, the method constructs coarse problem-adapted ansatz spaces by solving auxiliary problems on local subdomains. More precisely, our approach is based on the strategy presented by Maier [SIAM J. Numer. Anal. 59(2), 2021]. The unique selling point of the proposed method is an improved localization strategy curing the effect of deteriorating errors with respect to the mesh size when the local subdomains are not large enough. We present a rigorous a priori error analysis and demonstrate the performance of the method in a series of numerical experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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