LD-GCN couples an encoder-free latent-space neural ODE with a graph convolutional decoder, achieving accurate reduced-order modeling of time-dependent parameterized PDEs and detecting bifurcations from the latent trajectories.
Bonneville et al.Extrapolating Phase-Field Simulations in Space and Time with Purely Convolu- tional Architectures
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Latent Dynamics Graph Convolutional Networks for model order reduction of parameterized time-dependent PDEs
LD-GCN couples an encoder-free latent-space neural ODE with a graph convolutional decoder, achieving accurate reduced-order modeling of time-dependent parameterized PDEs and detecting bifurcations from the latent trajectories.