TI-PINN: Topological-identifier physics-informed neural networks for forward and inverse problems of discontinuous coefficient differential equations

2026/07/16 by Yang Liu, Dong Liu, Yi-Xiang Wang +5
Physics and Astronomy · Engineering · Mathematics · #Model Reduction and Neural Networks #Topology Optimization in Engineering #Numerical methods in inverse problems

paper · doi:10.1016/j.cam.2026.117964

Citations

• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
• When and why PINNs fail to train: A neural tangent kernel perspective
• A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations