3 citations · 5 across the 5 of their papers we have counts for
5 papers
Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems
Yongsheng Chen, Yong Chen, Wei Guo +1
Physics-informed neural networks (PINNs) provide a promising framework for solving inverse problems governed by partial differential equations (PDEs) by integrating observational d…
VC-PINN: Variable Coefficient Physical Information Neural Network For Forward And Inverse PDE Problems with Variable Coefficient
Zhengwu Miao, Yong Chen
The paper proposes a deep learning method specifically dealing with the forward and inverse problem of variable coefficient partial differential equations -- Variable Coefficient P…
Gradient-enhanced physics-informed neural networks based on transfer learning for inverse problems of the variable coefficient differential equations
Shuning Lin, Yong Chen
We propose gradient-enhanced PINNs based on transfer learning (TL-gPINNs) for inverse problems of the function coefficient discovery in order to overcome deficiency of the discrete…
Data-driven forward-inverse problems for the variable coefficients Hirota equation using deep learning method
Huijuan Zhou, Juncai Pu, Yong Chen
Data-driven forward-inverse problems for the variable coefficients Hirota (VCH) equation are discussed in this paper. The main idea is to use the improved physics-informed neural n…
Soliton, Breather and Rogue Wave Solutions for Solving the Nonlinear Schrödinger Equation Using a Deep Learning Method with Physical Constraints
Juncai Pu, Jun Li, Yong Chen
The nonlinear Schrodinger equation is a classical integrable equation which contains plenty of significant properties and occurs in many physical areas. However, due to the difficu…