Data-driven multiscale modeling for correcting dynamical systems
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866915695303852032 |
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| author | Otness, Karl Zanna, Laure Bruna, Joan |
| author_facet | Otness, Karl Zanna, Laure Bruna, Joan |
| contents | 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. We envision this method being generally applicable to problems with significant self-similarity or in which the prediction task is challenging and where stability of a learned model's impact on the target dynamical system is important. We evaluate our approach on a climate subgrid parameterization task in which our multiscale networks correct chaotic underlying models to reflect the contributions of unresolved, fine-scale dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_17496 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Data-driven multiscale modeling for correcting dynamical systems Otness, Karl Zanna, Laure Bruna, Joan Atmospheric and Oceanic Physics Machine Learning 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. We envision this method being generally applicable to problems with significant self-similarity or in which the prediction task is challenging and where stability of a learned model's impact on the target dynamical system is important. We evaluate our approach on a climate subgrid parameterization task in which our multiscale networks correct chaotic underlying models to reflect the contributions of unresolved, fine-scale dynamics. |
| title | Data-driven multiscale modeling for correcting dynamical systems |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2303.17496 |