Certified Neural Approximations of Nonlinear Dynamics
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908564564475904 |
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| author | Mathiesen, Frederik Baymler Vertovec, Nikolaus Fabiano, Francesco Laurenti, Luca Abate, Alessandro |
| author_facet | Mathiesen, Frederik Baymler Vertovec, Nikolaus Fabiano, Francesco Laurenti, Luca Abate, Alessandro |
| contents | Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems. However, in safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system. To address this fundamental challenge, we propose a novel, adaptive, and parallelizable verification method based on certified first-order models. Our approach provides formal error bounds on the neural approximations of dynamical systems, allowing them to be safely employed as surrogates by interpreting the error bound as bounded disturbances acting on the approximated dynamics. We demonstrate the effectiveness and scalability of our method on a range of established benchmarks from the literature, showing that it significantly outperforms the state-of-the-art. Furthermore, we show that our framework can successfully address additional scenarios previously intractable for existing methods - neural network compression and an autoencoder-based deep learning architecture for learning Koopman operators for the purpose of trajectory prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Certified Neural Approximations of Nonlinear Dynamics Mathiesen, Frederik Baymler Vertovec, Nikolaus Fabiano, Francesco Laurenti, Luca Abate, Alessandro Machine Learning Systems and Control Neural networks hold great potential to act as approximate models of nonlinear dynamical systems, with the resulting neural approximations enabling verification and control of such systems. However, in safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system. To address this fundamental challenge, we propose a novel, adaptive, and parallelizable verification method based on certified first-order models. Our approach provides formal error bounds on the neural approximations of dynamical systems, allowing them to be safely employed as surrogates by interpreting the error bound as bounded disturbances acting on the approximated dynamics. We demonstrate the effectiveness and scalability of our method on a range of established benchmarks from the literature, showing that it significantly outperforms the state-of-the-art. Furthermore, we show that our framework can successfully address additional scenarios previously intractable for existing methods - neural network compression and an autoencoder-based deep learning architecture for learning Koopman operators for the purpose of trajectory prediction. |
| title | Certified Neural Approximations of Nonlinear Dynamics |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2505.15497 |