RBF-Assisted Hybrid Neural Network for Solving Partial Differential Equations

Ying Li, Wei Gao and Shihui Ying ()
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Ying Li: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Wei Gao: School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Shihui Ying: Department of Mathematics, School of Science, Shanghai University, Shanghai 200444, China

Mathematics, 2024, vol. 12, issue 11, 1-25

Abstract: In scientific computing, neural networks have been widely used to solve partial differential equations (PDEs). In this paper, we propose a novel RBF-assisted hybrid neural network for approximating solutions to PDEs. Inspired by the tendency of physics-informed neural networks (PINNs) to become local approximations after training, the proposed method utilizes a radial basis function (RBF) to provide the normalization and localization properties to the input data. The objective of this strategy is to assist the network in solving PDEs more effectively. During the RBF-assisted processing part, the method selects the center points and collocation points separately to effectively manage data size and computational complexity. Subsequently, the RBF processed data are put into the network for predicting the solutions to PDEs. Finally, a series of experiments are conducted to evaluate the novel method. The numerical results confirm that the proposed method can accelerate the convergence speed of the loss function and improve predictive accuracy.

Keywords: partial differential equations; radial basis function; physics-informed neural networks; numerical solution (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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