Adversarial Autoencoders in Operator Learning

Dustin Enyeart; Guang Lin

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arXiv · 2412.07811

Adversarial Autoencoders in Operator Learning

Dustin Enyeart·Guang Lin

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Abstract

DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning. In this paper, the use an adversarial addition for these two neural operator architectures is studied.

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Dustin Enyeart, Guang Lin. 2024-12-10. Adversarial Autoencoders in Operator Learning. https://arxiv.org/abs/2412.07811

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