Machine learning identifies nullclines in oscillatory dynamical systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915206635978752 |
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| author | Prokop, Bartosz Billen, Jimmy Frolov, Nikita Gelens, Lendert |
| author_facet | Prokop, Bartosz Billen, Jimmy Frolov, Nikita Gelens, Lendert |
| contents | We introduce CLINE (Computational Learning and Identification of Nullclines), a neural network-based method that uncovers the hidden structure of nullclines from oscillatory time series data. Unlike traditional approaches aiming at direct prediction of system dynamics, CLINE identifies static geometric features of the phase space that encode the (non)linear relationships between state variables. It overcomes challenges such as multiple time scales and strong nonlinearities while producing interpretable results convertible into symbolic differential equations. We validate CLINE on various oscillatory systems, showcasing its effectiveness. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_16240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine learning identifies nullclines in oscillatory dynamical systems Prokop, Bartosz Billen, Jimmy Frolov, Nikita Gelens, Lendert Machine Learning Dynamical Systems Adaptation and Self-Organizing Systems Computational Physics We introduce CLINE (Computational Learning and Identification of Nullclines), a neural network-based method that uncovers the hidden structure of nullclines from oscillatory time series data. Unlike traditional approaches aiming at direct prediction of system dynamics, CLINE identifies static geometric features of the phase space that encode the (non)linear relationships between state variables. It overcomes challenges such as multiple time scales and strong nonlinearities while producing interpretable results convertible into symbolic differential equations. We validate CLINE on various oscillatory systems, showcasing its effectiveness. |
| title | Machine learning identifies nullclines in oscillatory dynamical systems |
| topic | Machine Learning Dynamical Systems Adaptation and Self-Organizing Systems Computational Physics |
| url | https://arxiv.org/abs/2503.16240 |