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自适应边界元法的估计器缩减与收敛性

Estimator reduction and convergence of adaptive BEM.

作者信息

Aurada Markus, Ferraz-Leite Samuel, Praetorius Dirk

机构信息

Vienna University of Technology, Institute for Analysis and Scientific Computing, Wiedner Hauptstr. 8-10, 1040 Wien, Austria.

出版信息

Appl Numer Math. 2012 Jun;62(6):787-801. doi: 10.1016/j.apnum.2011.06.014.

Abstract

A posteriori error estimation and related adaptive mesh-refining algorithms have themselves proven to be powerful tools in nowadays scientific computing. Contrary to adaptive finite element methods, convergence of adaptive boundary element schemes is, however, widely open. We propose a relaxed notion of convergence of adaptive boundary element schemes. Instead of asking for convergence of the error to zero, we only aim to prove estimator convergence in the sense that the adaptive algorithm drives the underlying error estimator to zero. We observe that certain error estimators satisfy an estimator reduction property which is sufficient for estimator convergence. The elementary analysis is only based on Dörfler marking and inverse estimates, but not on reliability and efficiency of the error estimator at hand. In particular, our approach gives a first mathematical justification for the proposed steering of anisotropic mesh-refinements, which is mandatory for optimal convergence behavior in 3D boundary element computations.

摘要

后验误差估计及相关的自适应网格细化算法已被证明是当今科学计算中的强大工具。然而,与自适应有限元方法相反,自适应边界元格式的收敛性问题仍广泛存在。我们提出了一种关于自适应边界元格式收敛性的宽松概念。我们并非要求误差收敛到零,而仅旨在证明估计器收敛,即自适应算法将底层误差估计器驱动到零。我们观察到某些误差估计器满足一种估计器缩减性质,这对于估计器收敛而言是充分的。基本分析仅基于 Dörfler 标记和逆估计,而不基于手头误差估计器的可靠性和效率。特别地,我们的方法为所提出的各向异性网格细化控制提供了首个数学依据,这对于三维边界元计算中的最优收敛行为而言是必不可少的。

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引用本文的文献

1
Axioms of adaptivity.适应性公理。
Comput Math Appl. 2014 Apr;67(6):1195-1253. doi: 10.1016/j.camwa.2013.12.003.
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