A decoupled parametric PINN with conditional modulation and Rosenthal-derived output scaling achieves zero-shot thermal inference across arbitrary metal alloys in laser powder bed fusion.
Parameterized physics- informed neural networks for parameterized pdes
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cs.LG 3years
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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.