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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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Spelling suggestions: "subject:"koopman autoencoder"" "subject:"koopmans autoencoder""

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Some Studies in Operator Learning for Solving Differential Equations

Dustin Lee Enyeart (20363187) 10 December 2024 (has links)
<pre>Operator learning has the potential to supplement traditional numerical methods, especially when speed is desired more than accuracy. <br>This includes the architectures DeepONets, Fourier neural operators and Koopman autoencoders.<br>First, this dissertation provides the background material for operator learning. <br>Then, it studies some general best practices for operator learning.<br>Then, it studies the loss functions and operator forms for Koopman autoencoders. <br>Finally, it studies the use of an adversarial addition to neural operators that have an autoencoder structure.</pre><p></p>
operator learning
differential equation
neural network
loss function
Koopman autoencoder
DeepONet
Fourier neural operator
adversarial autoencoder

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