Adding adversarial autoencoder training improves DeepONet and Koopman autoencoder accuracy by 4% to 27% on five small-data benchmarks.
Some Best Practices in Operator Learning
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abstract
Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It considers the architectures DeepONets, Fourier neural operators and Koopman autoencoders for several differential equations to find robust trends. Some options considered are activation functions, dropout and stochastic weight averaging.
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Adversarial Autoencoders in Operator Learning
Adding adversarial autoencoder training improves DeepONet and Koopman autoencoder accuracy by 4% to 27% on five small-data benchmarks.