LAM-PINN clusters PDE tasks via learning-affinity metrics and uses modular subnetworks to cut MSE by 19.7x on unseen tasks while using only 10% of conventional PINN training iterations.
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A structure-preserving GNN solver for parametric hyperbolic conservation laws achieves superior long-horizon stability and orders-of-magnitude speedups over high-resolution simulations on supersonic flow benchmarks.
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Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
LAM-PINN clusters PDE tasks via learning-affinity metrics and uses modular subnetworks to cut MSE by 19.7x on unseen tasks while using only 10% of conventional PINN training iterations.
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A Structure-Preserving Graph Neural Solver for Parametric Hyperbolic Conservation Laws
A structure-preserving GNN solver for parametric hyperbolic conservation laws achieves superior long-horizon stability and orders-of-magnitude speedups over high-resolution simulations on supersonic flow benchmarks.