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arXiv 1301.3203math.NAcs.NA
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间断系数椭圆问题的自适应有限元方法

Adaptive Finite Element Methods for Elliptic Problems with Discontinuous Coefficients

Texas A&M University(德克萨斯农工大学);University of Maryland(马里兰大学)

Andrea Bonito, Ronald A. DeVore, Ricardo H. Nochetto

AI总结:

针对具有间断扩散系数的椭圆型偏微分方程,提出基于L_q范数系数扰动的自适应有限元方法,并证明其计算最优性。

摘要:

具有间断扩散系数的椭圆型偏微分方程出现在多孔介质扩散、异质介质中的电磁场传播以及粗糙表面扩散过程等应用领域。使用有限元方法数值处理此类问题的标准方法是假设间断位于初始三角剖分的单元边界上。然而,这并不适用于间断出现在曲线、曲面或流形上的应用,甚至可能事先未知。处理此类间断问题的障碍之一是,椭圆型偏微分方程的通常扰动理论假设系数在$L_\infty$范数下的畸变有界,这进而要求在系数逼近时精确匹配间断点。我们提出了一种基于$L_q$范数($q<\infty$)系数畸变的新方法,因此不需要精确匹配间断点。然后,我们利用这一新的畸变理论,为这类间断问题制定了新的自适应有限元方法。我们证明了此类自适应有限元方法在畸变与计算次数之比的意义上是最优的,并报告了支持我们分析的有洞察力的数值结果。

英文摘要:

Elliptic partial differential equations (PDEs) with discontinuous diffusion coefficients occur in application domains such as diffusions through porous media, electro-magnetic field propagation on heterogeneous media, and diffusion processes on rough surfaces. The standard approach to numerically treating such problems using finite element methods is to assume that the discontinuities lie on the boundaries of the cells in the initial triangulation. However, this does not match applications where discontinuities occur on curves, surfaces, or manifolds, and could even be unknown beforehand. One of the obstacles to treating such discontinuity problems is that the usual perturbation theory for elliptic PDEs assumes bounds for the distortion of the coefficients in the $L_\infty$ norm and this in turn requires that the discontinuities are matched exactly when the coefficients are approximated. We present a new approach based on distortion of the coefficients in an $L_q$ norm with $q<\infty$ which therefore does not require the exact matching of the discontinuities. We then use this new distortion theory to formulate new adaptive finite element methods (AFEMs) for such discontinuity problems. We show that such AFEMs are optimal in the sense of distortion versus number of computations, and report insightful numerical results supporting our analysis.

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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