A feed-forward parameter-to-latent mapping plus a parameter-conditioned Neural ODE decouples space, time, and PDE parameters in a single physics-informed surrogate.
Parameterized physics- informed neural networks for parameterized pdes
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
One physics-informed network trained on the heat equation, not labeled data, predicts laser-scan temperature fields for unseen alloys including copper with ~1% relative error.
citing papers explorer
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Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
A feed-forward parameter-to-latent mapping plus a parameter-conditioned Neural ODE decouples space, time, and PDE parameters in a single physics-informed surrogate.
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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.
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Material-agnostic temperature field prediction for metal additive manufacturing via a parametric PINN framework
One physics-informed network trained on the heat equation, not labeled data, predicts laser-scan temperature fields for unseen alloys including copper with ~1% relative error.