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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.14821 |
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| _version_ | 1866916370946457600 |
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| author | Chen, Yuan Xiu, Dongbin |
| author_facet | Chen, Yuan Xiu, Dongbin |
| contents | We present a numerical method for learning the dynamics of slow components of unknown multiscale stochastic dynamical systems. While the governing equations of the systems are unknown, bursts of observation data of the slow variables are available. By utilizing the observation data, our proposed method is capable of constructing a generative stochastic model that can accurately capture the effective dynamics of the slow variables in distribution. We present a comprehensive set of numerical examples to demonstrate the performance of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14821 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems Chen, Yuan Xiu, Dongbin Machine Learning Numerical Analysis 60H10, 60H35, 62M45, 65C30 We present a numerical method for learning the dynamics of slow components of unknown multiscale stochastic dynamical systems. While the governing equations of the systems are unknown, bursts of observation data of the slow variables are available. By utilizing the observation data, our proposed method is capable of constructing a generative stochastic model that can accurately capture the effective dynamics of the slow variables in distribution. We present a comprehensive set of numerical examples to demonstrate the performance of the proposed method. |
| title | Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems |
| topic | Machine Learning Numerical Analysis 60H10, 60H35, 62M45, 65C30 |
| url | https://arxiv.org/abs/2408.14821 |