Learning Parametric Koopman Decompositions for Prediction and Control
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866915010928705536 |
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| author | Guo, Yue Korda, Milan Kevrekidis, Ioannis G. Li, Qianxiao |
| author_facet | Guo, Yue Korda, Milan Kevrekidis, Ioannis G. Li, Qianxiao |
| contents | We present an approach to construct approximate Koopman-type decompositions for dynamical systems depending on static or time-varying parameters. Our method simultaneously constructs an invariant subspace and a parametric family of projected Koopman operators acting on this subspace. We parametrize both the projected Koopman operator family and the dictionary that spans the invariant subspace by neural networks and jointly train them with trajectory data. We show theoretically the validity of our approach, and demonstrate via numerical experiments that it exhibits significant improvements over existing methods in solving prediction problems, especially those with large state or parameter dimensions, and those possessing strongly non-linear dynamics. Moreover, our method enables data-driven solution of optimal control problems involving non-linear dynamics, with interesting implications on controllability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_01124 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning Parametric Koopman Decompositions for Prediction and Control Guo, Yue Korda, Milan Kevrekidis, Ioannis G. Li, Qianxiao Optimization and Control 47N70, 37N35, 49M99 We present an approach to construct approximate Koopman-type decompositions for dynamical systems depending on static or time-varying parameters. Our method simultaneously constructs an invariant subspace and a parametric family of projected Koopman operators acting on this subspace. We parametrize both the projected Koopman operator family and the dictionary that spans the invariant subspace by neural networks and jointly train them with trajectory data. We show theoretically the validity of our approach, and demonstrate via numerical experiments that it exhibits significant improvements over existing methods in solving prediction problems, especially those with large state or parameter dimensions, and those possessing strongly non-linear dynamics. Moreover, our method enables data-driven solution of optimal control problems involving non-linear dynamics, with interesting implications on controllability. |
| title | Learning Parametric Koopman Decompositions for Prediction and Control |
| topic | Optimization and Control 47N70, 37N35, 49M99 |
| url | https://arxiv.org/abs/2310.01124 |