Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators
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| Title: | Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators |
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| Authors: | Mangeleer, Victor, Louppe, Gilles |
| Contributors: | Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège |
| Source: | Machine Learning and the Physical Sciences, NeurIPS 2023, New Orleans, United States [US], December 15, 2023 |
| Publication Year: | 2023 |
| Subject Terms: | Computer Science - Learning, Physics - Atmospheric and Oceanic Physics, Deep Learning, Fourier Neural Operators, Parameterizations, PyQG, Engineering, computing & technology, Electrical & electronics engineering, Ingénierie, informatique & technologie, Ingénierie électrique & électronique |
| Description: | In climate simulations, small-scale processes shape ocean dynamics but remaincomputationally expensive to resolve directly. For this reason, theircontributions are commonly approximated using empirical parameterizations,which lead to significant errors in long-term projections. In this work, wedevelop parameterizations based on Fourier Neural Operators, showcasing theiraccuracy and generalizability in comparison to other approaches. Finally, wediscuss the potential and limitations of neural networks operating in thefrequency domain, paving the way for future investigation. |
| Document Type: | conference poster not in proceedings http://purl.org/coar/resource_type/c_18co conferencePoster peer reviewed |
| Language: | English |
| Relation: | https://arxiv.org/abs/2310.02691; https://arxiv.org/abs/2310.02691 |
| Access URL: | https://orbi.uliege.be/handle/2268/309230 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Accession Number: | edsorb.309230 |
| Database: | ORBi |
| Abstract: | In climate simulations, small-scale processes shape ocean dynamics but remaincomputationally expensive to resolve directly. For this reason, theircontributions are commonly approximated using empirical parameterizations,which lead to significant errors in long-term projections. In this work, wedevelop parameterizations based on Fourier Neural Operators, showcasing theiraccuracy and generalizability in comparison to other approaches. Finally, wediscuss the potential and limitations of neural networks operating in thefrequency domain, paving the way for future investigation. |
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