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首页> 外文期刊>Applied mathematics and computation >A meshless discrete collocation method for the numerical solution of singular-logarithmic boundary integral equations utilizing radial basis functions
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A meshless discrete collocation method for the numerical solution of singular-logarithmic boundary integral equations utilizing radial basis functions

机译:利用径向基函数的奇异对数边界积分方程数值解的无网束离散搭配方法

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The main intention of the current paper is to describe a scheme for the numerical solution of boundary integral equations of the second kind with logarithmic singular kernels. These types of integral equations result from boundary value problems of Laplace's equations with linear Robin boundary conditions. The method approximates the solution using the radial basis function (RBF) expansion with polynomial precision in the discrete collocation method. The collocation method for solving logarithmic boundary integral equations encounters more difficulties for computing the singular integrals which cannot be approximated by the classical quadrature formulae. To overcome this problem, we utilize the non-uniform composite Gauss-Legendre integration rule and employ it to estimate the singular logarithm integrals appeared in the method. Since the scheme is based on the use of scattered points spread on the analyzed domain and does not need any domain elements, we can call it as the meshless discrete collocation method. The new algorithm is successful and easy to solve various types of boundary integral equations with singular kernels. We also provide the error estimate of the proposed method. The efficiency and accuracy of the new approach are illustrated by some numerical examples. (C) 2017 Elsevier Inc. All rights reserved.
机译:目前纸张的主要目的是描述具有对数奇异核的第二类边界积分方程的数值解的方案。这些类型的整体方程由Laplace的方程的边值问题具有线性Robin边界条件导致。该方法通过在离散搭配方法中使用多项式精度来近似于使用径向基函数(RBF)膨胀来近似溶液。用于求解对数边界积分方程的搭配方法遇到用于计算不能被经典正交公式近似的奇异积分的更大困难。为了克服这个问题,我们利用了非统一的复合高斯传奇集成规则,并采用它来估计方法中出现的奇异对数积分。由于该方案基于在分析的域上扩展的散射点的使用并且不需要任何域元素,因此我们可以称为无网格离散搭配方法。新算法成功且易于利用奇异内核解决各种类型的边界整体方程。我们还提供了所提出的方法的误差估计。一些数值例子说明了新方法的效率和准确性。 (c)2017年Elsevier Inc.保留所有权利。

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