HSPINN enforces Dirichlet and periodic BCs exactly via analytical lifting and masking, applies adaptive softmax weighting to soft loss terms for PDE residuals, and reports faster convergence and higher accuracy than standard PINNs on Poisson, Burgers, and convection problems.
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Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving
HSPINN enforces Dirichlet and periodic BCs exactly via analytical lifting and masking, applies adaptive softmax weighting to soft loss terms for PDE residuals, and reports faster convergence and higher accuracy than standard PINNs on Poisson, Burgers, and convection problems.