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.
Hard boundary condition enforcement in pinns using signed distance and neural basis functions.Computer Methods in Applied Mechanics and Engineering, 417:116542, 2023
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.