Data-driven multiscale modeling for correcting dynamical systems

• URL: http://arxiv.org/abs/2303.17496v2
• Date: Wed, 14 May 2025 14:04:18 GMT
• Title: Data-driven multiscale modeling for correcting dynamical systems
• Authors: Karl Otness, Laure Zanna, Joan Bruna,
• Abstract summary: 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.<n>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.
• Score: 32.986783465299084
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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