Integrated radial basis functions‐based differential quadrature method and its performance

In this paper, indirect radial basis function networks (IRBFN) proposed by Nam and Tranh (Neural Networks 2001; 14(2):185–199; Appl. Math. Modelling 2003; 27:197–220) are incorporated into the differential quadrature (DQ) approximation of derivatives. For simplicity, this new variant of RBF-DQ approach is named as iRBF-DQ method. The proposed approach is validated by its application to solve the one-dimensional Burger's equation, and simulate natural convection in a concentric annulus by solving Navier–Stokes equations. It was found that as compared to the benchmark data, the iRBF-DQ approach can provide more accurate results than the original RBF-DQ method. Copyright © 2006 John Wiley & Sons, Ltd.

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