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Subgrid parameterizations, which represent physical processes occurring below the resolu- tion of current climate models, are an important component in producing accurate, long-term predictions for the climate. A variety of approaches have been tested to design these com- ponents, including deep learning methods. In this work, we evaluate a proof of concept illustrating a multiscale approach to this prediction problem. We train neural networks to predict subgrid forcing values on a testbed model and examine improvements in prediction accuracy that can be obtained by using additional information in both fine-to-coarse and coarse-to-fine directions.
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This content will become publicly available on December 19, 2026
Data-driven multiscale modeling for correcting dynamical systems
Abstract We propose a multiscale approach for predicting quantities in dynamical systems which is explicitly structured to extract information in both fine-to-coarse and coarse-to-fine directions. Our approach improves model accuracy and stability with minimally increased computation compared to non-multiscale approaches with analogous network architecture. We evaluate our approach on an idealized fluid subgrid parameterization (known as closure) task in which our multiscale networks correct chaotic underlying models to reflect the contributions of unresolved, fine-scale dynamics.
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- Award ID(s):
- 1901091
- PAR ID:
- 10655977
- Publisher / Repository:
- Machine Learning Science and Technology
- Date Published:
- Journal Name:
- Machine Learning: Science and Technology
- Volume:
- 6
- Issue:
- 4
- ISSN:
- 2632-2153
- Page Range / eLocation ID:
- 045072
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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