曲面向量-拉普拉斯特征问题的有限元方法分析

A. Reusken
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引用次数: 3

摘要

本文研究了曲面向量-拉普拉斯特征问题的有限元离散化。我们考虑了两类已知的有限元方法,即一种基于Dziuk-Elliott曲面有限元方法的矢量类比,另一种基于所谓的微量有限元技术。这两类方法的一个关键成分是惩罚方法,该方法用于在弱意义上加强向量场的切向性。这种惩罚和由表面的数值近似引起的扰动导致向量-拉普拉斯特征问题的变分公式的离散化中的基本不整合。我们给出了一个适用于这类特征问题的非一致性离散化的一般抽象框架。导出了依赖于一定一致性和近似性参数的特征值和特征向量近似的误差界。讨论了这些边界的清晰度。数值实验结果说明了这种曲面向量-拉普拉斯特征问题的有限元离散化具有一定的收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of finite element methods for surface vector-Laplace eigenproblems
In this paper we study finite element discretizations of a surface vector-Laplace eigenproblem. We consider two known classes of finite element methods, namely one based on a vector analogon of the Dziuk-Elliott surface finite element method and one based on the so-called trace finite element technique. A key ingredient in both classes of methods is a penalization method that is used to enforce tangentiality of the vector field in a weak sense. This penalization and the perturbations that arise from numerical approximation of the surface lead to essential nonconformities in the discretization of the variational formulation of the vector-Laplace eigenproblem. We present a general abstract framework applicable to such nonconforming discretizations of eigenproblems. Error bounds both for eigenvalue and eigenvector approximations are derived that depend on certain consistency and approximability parameters. Sharpness of these bounds is discussed. Results of a numerical experiment illustrate certain convergence properties of such finite element discretizations of the surface vector-Laplace eigenproblem.
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