LaSDI-IT learns latent linear dynamics for interface tracking via a revised autoencoder and Gaussian process interpolation, achieving under 9% error and 106x speedup on shock-induced pore collapse in high explosives.
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A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
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Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
LaSDI-IT learns latent linear dynamics for interface tracking via a revised autoencoder and Gaussian process interpolation, achieving under 9% error and 106x speedup on shock-induced pore collapse in high explosives.
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Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.