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arXiv 1710.00529math.NAcs.NA
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非线性不连续Petrov-Galerkin方法

Nonlinear discontinuous Petrov-Galerkin methods

Humboldt-Universität zu Berlin(柏林洪堡大学);Gottfried Wilhelm Leibniz Universität Hannover(汉诺威莱布尼茨大学)

Carsten Carstensen, Philipp Bringmann, Friederike Hellwig, Peter Wriggers

AI总结:

本文提出非线性不连续Petrov-Galerkin方法,用于解决非线性问题,通过混合形式、缩减形式和加权非线性最小二乘法等方法,给出先验和后验误差估计。

摘要:

不连续Petrov-Galerkin方法是一种具有断裂测试空间的最小残差方法,本文将其应用于非线性模型问题。其最低阶版本适用于非线性均匀凸模型示例,并等价地表征为混合形式、缩减形式和加权非线性最小二乘法。对于抽象非线性dPG框架,获得了准最优的先验估计和可靠且高效的后验估计。变分模型示例允许在不精确求解的情况下实现保证的误差控制。数值示例中监测了离散极小值的细微唯一性。

英文摘要:

The discontinuous Petrov-Galerkin method is a minimal residual method with broken test spaces and is introduced for a nonlinear model problem in this paper. Its lowest-order version applies to a nonlinear uniformly convex model example and is equivalently characterized as a mixed formulation, a reduced formulation, and a weighted nonlinear least-squares method. Quasi-optimal a priori and reliable and efficient a posteriori estimates are obtained for the abstract nonlinear dPG framework for the approximation of a regular solution. The variational model example allows for a built-in guaranteed error control despite inexact solve. The subtle uniqueness of discrete minimizers is monitored in numerical examples.

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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