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arXiv 1409.3474math.NAcs.NA
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一种自适应广义多尺度不连续伽辽金方法(GMsDGM)用于高对比流体问题

An adaptive generalized multiscale discontinuous Galerkin method (GMsDGM) for high-contrast flow problems

The Chinese University of Hong Kong(香港中文大学);Texas A&M University(德克萨斯农工大学);King Abdullah University of Science and Technology (KAUST)(阿卜杜拉国王科技大学)

Eric T. Chung, Yalchin Efendiev, Wing Tat Leung

AI总结:

本文提出了一种自适应广义多尺度不连续伽辽金方法,推导了该方法的先验和后验误差估计,并开发了自适应富集算法以提高精度,同时讨论了自适应选择多尺度基函数的方法,展示了该方法的高效性和鲁棒性。

摘要:

本文开发了一种自适应广义多尺度不连续伽辽金方法(GMs-DGM)用于高对比流体问题,并推导了该方法的先验和后验误差估计。基于后验误差估计器,我们开发了自适应富集算法,并证明了其收敛性。自适应富集算法为需要更多基函数的区域提供了自动丰富近似空间,其表现优于均匀富集。我们还讨论了一种通过相关残差与多尺度基函数来自适应选择多尺度基函数的方法(参见[4])。所提出的误差指标基于L2范数,且计算成本低廉,使我们的方法高效。数值结果展示了所提出误差指标的鲁棒性。

英文摘要:

In this paper, we develop an adaptive Generalized Multiscale Discontinuous Galerkin Method (GMs-DGM) for a class of high-contrast flow problems, and derive a-priori and a-posteriori error estimates for the method. Based on the a-posteriori error estimator, we develop an adaptive enrichment algorithm for our GMsDGM and prove its convergence. The adaptive enrichment algorithm gives an automatic way to enrich the approximation space in regions where the solution requires more basis functions, which are shown to perform well compared with a uniform enrichment. We also discuss an approach that adaptively selects multiscale basis functions by correlating the residual to multiscale basis functions (cf. [4]). The proposed error indicators are L2-based and can be inexpensively computed which makes our approach efficient. Numerical results are presented that demonstrate the robustness of the proposed error indicators.

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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