Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published March 2023 | Published
Conference Paper Open

Neural Operators for Solving PDEs and Inverse Design

  • 1. ROR icon California Institute of Technology

Abstract

Deep learning surrogate models have shown promise in modeling complex physical phenomena such as photonics, fluid flows, molecular dynamics and material properties. However, standard neural networks assume finite-dimensional inputs and outputs, and hence, cannot withstand a change in resolution or discretization between training and testing. We introduce Fourier neural operators that can learn operators, which are mappings between infinite dimensional spaces. They are discretization-invariant and can generalize beyond the discretization or resolution of training data. They can efficiently solve partial differential equations (PDEs) on general geometries. We consider a variety of PDEs for both forward modeling and inverse design problems, as well as show practical gains in the lithography domain.

Additional Information

© 2023 Copyright is held by the owner/author(s).

Attached Files

Published - 3569052.3578911.pdf

Files

3569052.3578911.pdf
Files (710.9 kB)
Name Size Download all
md5:48104c8791550947831a81f1552b170c
710.9 kB Preview Download

Additional details

Created:
August 20, 2023
Modified:
November 8, 2023