稳健的 DPG 测试空间和福尔廷算子--[math] 和 [math] 案例

IF 2.8 2区 数学 Q1 MATHEMATICS, APPLIED
Thomas Führer, Norbert Heuer
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引用次数: 0

摘要

SIAM 数值分析期刊》第 62 卷第 2 期第 718-748 页,2024 年 4 月。 摘要。在完全离散设置下,具有最优检验函数的非连续 Petrov-Galerkin (DPG) 方法的稳定性需要局部检验空间,以确保 Fortin 算子的存在。我们为任意空间维度和任意多项式度的简约上的[math]和[math]构造了这样的算子。由此得到的检验空间比之前分析的情况要小。对于与参数相关的规范,我们通过包含面气泡函数来实现均匀有界性,这些函数在面上是多项式,在内部呈指数衰减。举例来说,我们考虑了反应主导扩散的典型 DPG 设置。我们的测试空间保证了方案的均匀稳定性和准最佳收敛性。我们展示了数值实验,说明在使用标准多项式测试空间时,对于小扩散系数,残差会失去稳定性和误差控制,而使用我们的构造,则会观察到均匀的稳定性和误差控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust DPG Test Spaces and Fortin Operators—The [math] and [math] Cases
SIAM Journal on Numerical Analysis, Volume 62, Issue 2, Page 718-748, April 2024.
Abstract. At the fully discrete setting, stability of the discontinuous Petrov–Galerkin (DPG) method with optimal test functions requires local test spaces that ensure the existence of Fortin operators. We construct such operators for [math] and [math] on simplices in any space dimension and arbitrary polynomial degree. The resulting test spaces are smaller than previously analyzed cases. For parameter-dependent norms, we achieve uniform boundedness by the inclusion of face bubble functions that are polynomials on faces and decay exponentially in the interior. As an example, we consider a canonical DPG setting for reaction-dominated diffusion. Our test spaces guarantee uniform stability and quasi-optimal convergence of the scheme. We present numerical experiments that illustrate the loss of stability and error control by the residual for small diffusion coefficient when using standard polynomial test spaces, whereas we observe uniform stability and error control with our construction.
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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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