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Meshless Galerkin algorithms for boundary integral equations with moving least square approximations

机译:具有移动最小二乘近似的边界积分方程的无网格Galerkin算法

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摘要

In this paper, we first give error estimates for the moving least square (MLS) approximation in the H~k norm in two dimensions when nodes and weight functions satisfy certain conditions. This two-dimensional error results can be applied to the surface of a three-dimensional domain. Then combining boundary integral equations (BIEs) and the MLS approximation, a meshless Galerkin algorithm, the Galerkin boundary node method (GBNM), is presented. The optimal asymptotic error estimates of the GBNM for three-dimensional BIEs are derived. Finally, taking the Dirichlet problem of Laplace equation as an example, we set up a framework for error estimates of the GBNM for boundary value problems in three dimensions.
机译:在本文中,当节点和权重函数满足一定条件时,我们首先给出二维H〜k范数中最小二乘(MLS)近似的误差估计。该二维误差结果可以应用于三维域的表面。然后结合边界积分方程(BIE)和MLS逼近,提出了一种无网格的Galerkin算法,即Galerkin边界节点法(GBNM)。导出了针对三维BIE的GBNM的最佳渐近误差估计。最后,以拉普拉斯方程的狄利克雷问题为例,建立了三维边界值问题GBNM误差估计的框架。

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