Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912325353603072 |
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| author | Williams, Emily Darmofal, David |
| author_facet | Williams, Emily Darmofal, David |
| contents | Traditional deterministic subgrid-scale (SGS) models are often dissipative and unstable, especially in regions of chaotic and turbulent flow. Ongoing work in climate science and ocean modeling motivates the use of stochastic SGS models for chaotic dynamics. Further, developing stochastic generative models of underlying dynamics is a rapidly expanding field. In this work, we aim to incorporate stochastic integration toward closure modeling for chaotic dynamical systems. Further, we want to explore the potential stabilizing effect that stochastic models could have on linearized chaotic systems. We propose parametric and generative approaches for closure modeling using stochastic differential equations (SDEs). We derive and implement a quadratic diffusion model based on the fluctuations, demonstrating increased accuracy from bridging theoretical models with generative approaches. Results are demonstrated on the Lorenz-63 dynamical system. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_09750 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems Williams, Emily Darmofal, David Numerical Analysis Machine Learning Traditional deterministic subgrid-scale (SGS) models are often dissipative and unstable, especially in regions of chaotic and turbulent flow. Ongoing work in climate science and ocean modeling motivates the use of stochastic SGS models for chaotic dynamics. Further, developing stochastic generative models of underlying dynamics is a rapidly expanding field. In this work, we aim to incorporate stochastic integration toward closure modeling for chaotic dynamical systems. Further, we want to explore the potential stabilizing effect that stochastic models could have on linearized chaotic systems. We propose parametric and generative approaches for closure modeling using stochastic differential equations (SDEs). We derive and implement a quadratic diffusion model based on the fluctuations, demonstrating increased accuracy from bridging theoretical models with generative approaches. Results are demonstrated on the Lorenz-63 dynamical system. |
| title | Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems |
| topic | Numerical Analysis Machine Learning |
| url | https://arxiv.org/abs/2504.09750 |