A coupled LSTM-GNN model reconstructs local elasto-plastic stress fields from macroscopic loading paths on a plate-with-hole microstructure, achieving 1000x speedup and mesh transferability with 1.9% error.
Kraus,Multi-Objective Loss Balancing for Physics-Informed Deep Learning, Computer Methods in Applied Mechanics and Engineering439(2025), 117914, DOI 10.1016/j.cma.2025.117914
3 Pith papers cite this work. Polarity classification is still indexing.
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Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
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A coupled LSTM-GNN model reconstructs local elasto-plastic stress fields from macroscopic loading paths on a plate-with-hole microstructure, achieving 1000x speedup and mesh transferability with 1.9% error.