Yun-Hu Wang

Physics-informed neural networks with Riccati-type pseudopotential constraints for solving nonlinear evolution equations.

Physics-informed neural networks (PINNs) provide a powerful framework for solving nonlinear evolution equations, but their accuracy and stability often deteriorate for systems with high-frequency structures, strong nonlinear interactions, or high-order derivatives. Although structure-informed extensions can improve phy...