Evaluation of Deep Neural Operator Models Toward Ocean Forecasting-Reference-Cited by-同舟云学术

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Published:2023-09-25 Issue: Volume: Page:
ISSN:
Container-title:OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
language:
Short-container-title:

Author:

Rajagopal Ellery¹,Babu Anantha N. S.²,Ryu Tony²,Haley Patrick J.²,Mirabito Chris²,Lermusiaux Pierre F. J.²

Affiliation:

1. Massachusetts Institute of Technology,Department of Electrical Engineering and Computer Science,Cambridge,MA

2. Center for Computational Science and Engineering, Massachusetts Institute of Technology,Department of Mechanical Engineering,Cambridge,MA

Funder

Office of Naval Research

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10336884/10336949/10337380.pdf?arnumber=10337380

Reference76 articles.

1. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

2. A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data

3. A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

4. Learning in latent spaces improves the predictive accuracy of deep neural operators;Kontolati;arXiv preprint,2023

5. Neural operator: Graph kernel network for partial differential equations;Li;arXiv preprint,2020

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