A trainable-by-parts variational autoencoder with a neural network mapper claims to beat FNO and DeepONet on groundwater flow problems in both accuracy and training efficiency.
Koml´ os–Major–Tusn´ ady approximation under dependence.The Annals of Probability, 42(2):794–817, 2014
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VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations
A trainable-by-parts variational autoencoder with a neural network mapper claims to beat FNO and DeepONet on groundwater flow problems in both accuracy and training efficiency.