Predicting symbolic ODEs from multiple trajectories
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908614592036864 |
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| author | Şahin, Yakup Emre Kilbertus, Niki Becker, Sören |
| author_facet | Şahin, Yakup Emre Kilbertus, Niki Becker, Sören |
| contents | We introduce MIO, a transformer-based model for inferring symbolic ordinary differential equations (ODEs) from multiple observed trajectories of a dynamical system. By combining multiple instance learning with transformer-based symbolic regression, the model effectively leverages repeated observations of the same system to learn more generalizable representations of the underlying dynamics. We investigate different instance aggregation strategies and show that even simple mean aggregation can substantially boost performance. MIO is evaluated on systems ranging from one to four dimensions and under varying noise levels, consistently outperforming existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting symbolic ODEs from multiple trajectories Şahin, Yakup Emre Kilbertus, Niki Becker, Sören Machine Learning We introduce MIO, a transformer-based model for inferring symbolic ordinary differential equations (ODEs) from multiple observed trajectories of a dynamical system. By combining multiple instance learning with transformer-based symbolic regression, the model effectively leverages repeated observations of the same system to learn more generalizable representations of the underlying dynamics. We investigate different instance aggregation strategies and show that even simple mean aggregation can substantially boost performance. MIO is evaluated on systems ranging from one to four dimensions and under varying noise levels, consistently outperforming existing baselines. |
| title | Predicting symbolic ODEs from multiple trajectories |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.23295 |