Data-driven Identification of Attractors Using Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915677267296256 |
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| author | Gameiro, Marcio Gelb, Brittany Kalies, William Kramar, Miroslav Mischaikow, Konstantin Tatasciore, Paul |
| author_facet | Gameiro, Marcio Gelb, Brittany Kalies, William Kramar, Miroslav Mischaikow, Konstantin Tatasciore, Paul |
| contents | In this paper we explore challenges in developing a topological framework in which machine learning can be used to robustly characterize global dynamics. Specifically, we focus on learning a useful discretization of the phase space of a flow on compact, hyperrectangle in $\mathbb{R}^n$ from a neural network trained on labeled orbit data. A characterization of the structure of the global dynamics is obtained from approximations of attracting neighborhoods provided by the phase space discretization. The perspective that motivates this work is based on Conley's topological approach to dynamics, which provides a means to evaluate the efficacy and efficiency of our approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06492 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-driven Identification of Attractors Using Machine Learning Gameiro, Marcio Gelb, Brittany Kalies, William Kramar, Miroslav Mischaikow, Konstantin Tatasciore, Paul Dynamical Systems 37M22, 37B35, 68T07 In this paper we explore challenges in developing a topological framework in which machine learning can be used to robustly characterize global dynamics. Specifically, we focus on learning a useful discretization of the phase space of a flow on compact, hyperrectangle in $\mathbb{R}^n$ from a neural network trained on labeled orbit data. A characterization of the structure of the global dynamics is obtained from approximations of attracting neighborhoods provided by the phase space discretization. The perspective that motivates this work is based on Conley's topological approach to dynamics, which provides a means to evaluate the efficacy and efficiency of our approach. |
| title | Data-driven Identification of Attractors Using Machine Learning |
| topic | Dynamical Systems 37M22, 37B35, 68T07 |
| url | https://arxiv.org/abs/2506.06492 |