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arXiv 1703.05136math.NAcs.NA
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混合高阶方法简介

An introduction to Hybrid High-Order methods

Institut Montpelliérain Alexander Grothendieck, CNRS, Univ. de Montpellier(蒙彼利埃亚历山大格罗滕迪克研究所,法国国家科学研究中心,蒙彼利埃大学)

Daniele A. Di Pietro, Roberta Tittarelli

AI总结:

本文介绍混合高阶(HHO)方法,该方法用于偏微分方程,具有任意逼近阶数、离散层面的连续性质保持以及计算成本降低等优势。通过泊松方程模型问题,详细阐述了构造方法,并证明了误差的先验收敛性和后验估计。还介绍了非线性p-拉普拉斯方程和标量扩散-对流-反应问题的离散化应用。

摘要:

本文介绍了混合高阶(HHO)方法,这是一种用于偏微分方程的新一代数值方法,具有多个优势:在通用多面体网格上支持任意逼近阶数,能够在离散层面再现相关连续性质,以及由于静态消元和紧凑Stencil而降低计算成本。在建立离散设置后,我们以泊松方程作为模型问题,介绍了HHO方法的基本原理。我们详细描述了构造过程,并证明了误差在各种范数下的先验收敛性以及能量范数的后验估计。随后,我们考虑了两个应用:非线性$p$-拉普拉斯方程和标量扩散-对流-反应问题的离散化。前者应用用于介绍紧致性分析技术以研究收敛到最小正则性解。后者应用用于介绍一阶算子的离散化和边界条件的弱强制。数值示例辅以阐述。

英文摘要:

This chapter provides an introduction to Hybrid High-Order (HHO) methods. These are new generation numerical methods for PDEs with several advantageous features: the support of arbitrary approximation orders on general polyhedral meshes, the reproduction at the discrete level of relevant continuous properties, and a reduced computational cost thanks to static condensation and compact stencil. After establishing the discrete setting, we introduce the basics of HHO methods using as a model problem the Poisson equation. We describe in detail the construction, and prove a priori convergence results for various norms of the error as well as a posteriori estimates for the energy norm. We then consider two applications: the discretization of the nonlinear $p$-Laplace equation and of scalar diffusion-advection-reaction problems. The former application is used to introduce compactness analysis techniques to study the convergence to minimal regularity solution. The latter is used to introduce the discretization of first-order operators and the weak enforcement of boundary conditions. Numerical examples accompany the exposition.

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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