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Some Best Practices in Operator Learning

2024-12-09Code Available0· sign in to hype

Dustin Enyeart, Guang Lin

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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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