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arXiv 1709.04679math.NAcs.NA
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一种混合高阶方法用于高度振荡椭圆问题

A Hybrid High-Order method for highly oscillatory elliptic problems

Université Paris-Est, CERMICS (ENPC)(巴黎东大学,CERMICS(国立桥梁与道路学院));Inria Paris(法国国家信息与自动化研究所巴黎分院);École Polytechnique Fédérale de Lausanne(洛桑联邦理工学院);Inria Lille - Nord Europe(法国国家信息与自动化研究所里尔-北欧分院)

Matteo Cicuttin, Alexandre Ern, Simon Lemaire

AI总结:

本文提出了一种混合高阶方法,用于解决高度振荡椭圆问题,该方法能够处理一般网格。通过局部静态消元和重建算子,实现高阶逼近,并在周期性系数下获得能量误差估计。

摘要:

我们为高度振荡椭圆问题设计了一种混合高阶(HHO)方法,能够处理一般网格。该方法基于离散未知数,即附着于粗网格面和单元的多项式;附着于单元的多项式可通过局部静态消元消除。主要构建成分是一个重建算子,局部于每个粗单元,将其映射到由振荡基函数张成的细尺度空间。所提出的HHO方法扩展了某些现有多尺度方法的思想,同时在一般网格上提供了首次完整的分析。它还改进了这些方法,利用HHO框架的灵活性。该方法能够处理任意阶数的逼近$k\geq 0$。对于面未知数,即阶数为$k$的多项式,我们设计了两种方法版本,取决于单元未知数的多项式阶数$(k-1)$或$k$。我们证明,在周期性系数情况下,能量误差估计形式为$\left(\varepsilon^{\frac{1}{2}}+H^{k+1}+(\varepsilon/H)^{\frac{1}{2}}\right)$,并在一些测试案例中展示了我们的理论发现。

英文摘要:

We devise a Hybrid High-Order (HHO) method for highly oscillatory elliptic problems that is capable of handling general meshes. The method hinges on discrete unknowns that are polynomials attached to the faces and cells of a coarse mesh; those attached to the cells can be eliminated locally using static condensation. The main building ingredient is a reconstruction operator, local to each coarse cell, that maps onto a fine-scale space spanned by oscillatory basis functions. The present HHO method generalizes the ideas of some existing multiscale approaches, while providing the first complete analysis on general meshes. It also improves on those methods, taking advantage of the flexibility granted by the HHO framework. The method handles arbitrary orders of approximation $k\geq 0$. For face unknowns that are polynomials of degree $k$, we devise two versions of the method, depending on the polynomial degree $(k-1)$ or $k$ of the cell unknowns. We prove, in the case of periodic coefficients, an energy-error estimate of the form $\left(\varepsilon^{\frac{1}{2}}+H^{k+1}+(\varepsilon/H)^{\frac{1}{2}}\right)$, and we illustrate our theoretical findings on some test-cases.

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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