Learning Neural Koopman Operators with Dissipativity Guarantees
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915530333487104 |
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| author | Xu, Yuezhu Sivaranjani, S. Gupta, Vijay |
| author_facet | Xu, Yuezhu Sivaranjani, S. Gupta, Vijay |
| contents | We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative. We propose a two-stage approach. First, we learn an unconstrained neural Koopman model that closely approximates the system dynamics. Then, we minimally perturb the parameters to enforce strict dissipativity. Crucially, we establish theoretical guarantees that extend the dissipativity properties of the learned model back to the original nonlinear system. We realize this by deriving an exact relationship between the dissipativity of the learned model and the true system through careful characterization of the identification errors from the noisy data, Koopman operator truncation, and generalization to unseen data. We demonstrate our approach through simulation on a Duffing oscillator model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07294 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Neural Koopman Operators with Dissipativity Guarantees Xu, Yuezhu Sivaranjani, S. Gupta, Vijay Systems and Control We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative. We propose a two-stage approach. First, we learn an unconstrained neural Koopman model that closely approximates the system dynamics. Then, we minimally perturb the parameters to enforce strict dissipativity. Crucially, we establish theoretical guarantees that extend the dissipativity properties of the learned model back to the original nonlinear system. We realize this by deriving an exact relationship between the dissipativity of the learned model and the true system through careful characterization of the identification errors from the noisy data, Koopman operator truncation, and generalization to unseen data. We demonstrate our approach through simulation on a Duffing oscillator model. |
| title | Learning Neural Koopman Operators with Dissipativity Guarantees |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.07294 |