Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	ProfessorDan Hu,Shanghai Jiao Tong Universit
Inviter:	Professor Haijun Yu
Title:	Residual-informed neural networks for partial differential equations
Language:	Chinese
Time & Venue:	2023.07.11 15:00-16:00 Z301
Abstract:	Deep learning has achieved wide success in solving Partial Differential Equations (PDEs), with particular strength in handling high dimensional problems and parametric problems. Nevertheless, there is still a lack of a clear picture on the designing of network architecture and the training of network parameters. In this work, we develop Residual-Informed Neural Networks (RINN) to solve partial differential equations. Compared to the widely used method PINN (Physics-Informed Neural Networks), RINN avoids calculating the high-order derivatives of neural networks, thus significantly reduces the computational cost.