An autoencoder plus latent neural ODE, trained with teacher forcing and autoregressive rollouts, beats several published neural PDE surrogates on benchmark equations while using fewer parameters and faster inference.
Continuous PDE dynamics forecasting with implicit neural representations
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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs
An autoencoder plus latent neural ODE, trained with teacher forcing and autoregressive rollouts, beats several published neural PDE surrogates on benchmark equations while using fewer parameters and faster inference.