Evaluation of Deep Neural Operator Models Toward Ocean Forecasting
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2308.11814.pdf
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Author(s)
Rajagopal, Ellery
Babu, Anantha N. S.
Ryu, Tony
Haley, Patrick J.
Mirabito, Chris
Lermusiaux, Pierre F. J.
Date Issued
September 25, 2023
Publisher
IEEE
Citation
Rajagopal, Ellery, Babu, Anantha N. S., Ryu, Tony, Haley, Patrick J., Mirabito, Chris et al. 2023. "Evaluation of Deep Neural Operator Models Toward Ocean Forecasting."
Version
Author's final manuscript
Abstract
Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications.
Description
OCEANS 2023 - MTS/IEEE U.S. Gulf Coast
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Center for Computational Science and Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.23919/oceans52994.2023.10337380
