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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.01816 |
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| _version_ | 1866929388504743936 |
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| author | Tenachi, Wassim Ibata, Rodrigo François, Thibaut L. Diakogiannis, Foivos I. |
| author_facet | Tenachi, Wassim Ibata, Rodrigo François, Thibaut L. Diakogiannis, Foivos I. |
| contents | We introduce 'Class Symbolic Regression' (Class SR) a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization ($Φ$-SO) framework for Symbolic Regression, which integrates dimensional analysis constraints and deep reinforcement learning for unsupervised symbolic analytical function discovery from data. Additionally, we introduce the first Class SR benchmark, comprising a series of synthetic physical challenges specifically designed to evaluate such algorithms. We demonstrate the efficacy of our novel approach by applying it to these benchmark challenges and showcase its practical utility for astrophysics by successfully extracting an analytic galaxy potential from a set of simulated orbits approximating stellar streams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01816 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Class Symbolic Regression: Gotta Fit 'Em All Tenachi, Wassim Ibata, Rodrigo François, Thibaut L. Diakogiannis, Foivos I. Machine Learning Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Computational Physics We introduce 'Class Symbolic Regression' (Class SR) a first framework for automatically finding a single analytical functional form that accurately fits multiple datasets - each realization being governed by its own (possibly) unique set of fitting parameters. This hierarchical framework leverages the common constraint that all the members of a single class of physical phenomena follow a common governing law. Our approach extends the capabilities of our earlier Physical Symbolic Optimization ($Φ$-SO) framework for Symbolic Regression, which integrates dimensional analysis constraints and deep reinforcement learning for unsupervised symbolic analytical function discovery from data. Additionally, we introduce the first Class SR benchmark, comprising a series of synthetic physical challenges specifically designed to evaluate such algorithms. We demonstrate the efficacy of our novel approach by applying it to these benchmark challenges and showcase its practical utility for astrophysics by successfully extracting an analytic galaxy potential from a set of simulated orbits approximating stellar streams. |
| title | Class Symbolic Regression: Gotta Fit 'Em All |
| topic | Machine Learning Astrophysics of Galaxies Instrumentation and Methods for Astrophysics Computational Physics |
| url | https://arxiv.org/abs/2312.01816 |