A meshless technique based on the radial basis functions for solving systems of partial differential equations

IF 1.1 Q2 MATHEMATICS, APPLIED
M. Nemati, M. Shafiee, H. Ebrahimi
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引用次数: 0

Abstract

The radial basis functions (RBFs) methods were first developed by Kansa to approximate partial differential equations (PDEs). The RBFs method is being truly meshfree becomes quite appealing, owing to the presence of distance function, straight-forward implementation, and ease of programming in higher dimensions. Another considerable advantage is the presence of a tunable free shape parameter, contained in most of the RBFs that control the accuracy of the RBFs method. Here, the solution of the two dimensional system of nonlinear partial differential equations is examined numerically by a Global Radial Basis Functions Collocation Method (GRBFCM). It can work on a set of random or uniform nodes with no need for element connectivity of input data. For the time-dependent partial differential equations, a system of ordinary differential equations (ODEs) is derived from this scheme. Like some other numerical methods, a comparison between numerical results with analytical solutions is implemented confirming the efficiency, accuracy, and simple performance of the suggested method.
求解偏微分方程组的一种基于径向基函数的无网格技术
径向基函数(RBF)方法最早由Kansa开发用于近似偏微分方程(PDE)。RBFs方法是真正的无网格方法,由于距离函数的存在、直接实现以及易于在更高维度上编程,因此变得非常有吸引力。另一个相当大的优点是存在可调的自由形状参数,该参数包含在控制RBF方法精度的大多数RBF中。本文用全局径向基函数配置法(GRBFCM)对二维非线性偏微分方程组的解进行了数值检验。它可以在一组随机或统一的节点上工作,而不需要输入数据的元素连接。对于含时偏微分方程,从该格式导出了一个常微分方程组。与其他一些数值方法一样,将数值结果与解析解进行了比较,证实了所建议方法的有效性、准确性和简单性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.20
自引率
27.30%
发文量
0
审稿时长
4 weeks
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