Xi-Wen Chen

A Comparative Study of Loss-Level Conservation Constraints in PINNs and Scheme-Level Structure-Preserving Approaches for Hamiltonian Systems

Physics-Informed Neural Networks (PINNs) provide a neural network-based, mesh-free computational method for solving partial differential equations (PDEs) by incorporating PDE residuals, initial conditions, and boundary conditions into the loss function. However, standard PINNs may still suffer from issues such as conse...