ReLU artificial neural networks for the grid adaptation in finite element method. Issue 1 (July 2021)

• Record Type: Journal Article
• Authors: Fu, Xuemei; Chen, Luoping; Wu, Fanyun
• Is Part Of: Journal of physics. Volume 1978:Issue 1(2021)
• Publication Date: 2021-07
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1978/1/012032
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, we study the rectified linear unit (ReLU) artificial neural network (ANN) for grid adaptation in finite element method, which is used for solving differential equations (DEs) with initial/boundary condition. Compared with the classical adaptive finite element method (AFEM), ReLU ANN based on finite element method can keep the number of grid-points constant but change their relative location. Our numerical experiments show that approximate solutions obtained from the classical finite element method by ReLU ANN are more accurate than those obtained by AFEM.