3 papers
cs.LG2024
Adversarial Autoencoders in Operator Learning
Dustin Enyeart, Guang Lin
DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved pe…
cs.LG2024
Some Best Practices in Operator Learning
Dustin Enyeart, Guang Lin
Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It consi…
cs.LG2024
Loss Terms and Operator Forms of Koopman Autoencoders
Dustin Enyeart, Guang Lin
Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presen…