椭圆边值问题的非拟合Trefftz不连续Galerkin方法

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Fabian Heimann, Christoph Lehrenfeld, Paul Stocker, Henry von Wahl
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引用次数: 1

摘要

提出了一种基于不连续Trefftz ansatz空间的几何不拟合有限元方法。Trefftz方法允许在不连续伽辽金方法中减少自由度的数量,因此,求解产生的线性系统的成本显着降低。这项工作表明,他们也是一个很好的方式来减少自由度的数量,在一个不拟合的设置。我们提出了一类具有不同稳定机制的几何不拟合不连续伽辽金方法的统一分析,以处理几何和网格之间的小切口。我们涵盖了稳定性,并以统一的方式推导了先验误差界,包括由模型泊松问题的离散类几何近似引起的误差。分析同样涵盖了Trefftz和全多项式ansatz空间。数值算例验证了理论结果,并证明了该方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unfitted Trefftz discontinuous Galerkin methods for elliptic boundary value problems
We propose a new geometrically unfitted finite element method based on discontinuous Trefftz ansatz spaces. Trefftz methods allow for a reduction in the number of degrees of freedom in discontinuous Galerkin methods, thereby, the costs for solving arising linear systems significantly. This work shows that they are also an excellent way to reduce the number of degrees of freedom in an unfitted setting. We present a unified analysis of a class of geometrically unfitted discontinuous Galerkin methods with different stabilisation mechanisms to deal with small cuts between the geometry and the mesh. We cover stability and derive a-priori error bounds, including errors arising from geometry approximation for the class of discretisations for a model Poisson problem in a unified manner. The analysis covers Trefftz and full polynomial ansatz spaces, alike. Numerical examples validate the theoretical findings and demonstrate the potential of the approach.
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来源期刊
Esaim-Probability and Statistics
Esaim-Probability and Statistics STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍: The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.
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