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 timedependent 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.
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Nemati, M., Shafiee, M., & Ebrahimi, H. (2022). A meshless technique based on the radial basis functions for solving systems of partial differential equations. Computational Methods for Differential Equations, 10(2), 526-537. doi: 10.22034/cmde.2021.39707.1740
MLA
Mehran Nemati; Mahmoud Shafiee; Hamideh Ebrahimi. "A meshless technique based on the radial basis functions for solving systems of partial differential equations". Computational Methods for Differential Equations, 10, 2, 2022, 526-537. doi: 10.22034/cmde.2021.39707.1740
HARVARD
Nemati, M., Shafiee, M., Ebrahimi, H. (2022). 'A meshless technique based on the radial basis functions for solving systems of partial differential equations', Computational Methods for Differential Equations, 10(2), pp. 526-537. doi: 10.22034/cmde.2021.39707.1740
VANCOUVER
Nemati, M., Shafiee, M., Ebrahimi, H. A meshless technique based on the radial basis functions for solving systems of partial differential equations. Computational Methods for Differential Equations, 2022; 10(2): 526-537. doi: 10.22034/cmde.2021.39707.1740
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