Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912706477424640 |
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| author | Shah, Saumya Khoo, Zi-Yu Yang, Abel Bressan, Stéphane |
| author_facet | Shah, Saumya Khoo, Zi-Yu Yang, Abel Bressan, Stéphane |
| contents | This work explores using the physics-inspired AI Feynman symbolic regression algorithm to automatically rediscover a fundamental equation in astronomy -- the Equation of the Centre. Through the introduction of observational and inductive biases corresponding to the physical nature of the system through data preprocessing and search space restriction, AI Feynman was successful in recovering the first-order analytical form of this equation from lunar ephemerides data. However, this manual approach highlights a key limitation in its reliance on expert-driven coordinate system selection. We therefore propose an automated preprocessing extension to find the canonical coordinate system. Results demonstrate that targeted domain knowledge embedding enables symbolic regression to rediscover physical laws, but also highlight further challenges in constraining symbolic regression to derive physics equations when leveraging domain knowledge through tailored biases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09979 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases Shah, Saumya Khoo, Zi-Yu Yang, Abel Bressan, Stéphane Machine Learning Instrumentation and Methods for Astrophysics I.2.1 This work explores using the physics-inspired AI Feynman symbolic regression algorithm to automatically rediscover a fundamental equation in astronomy -- the Equation of the Centre. Through the introduction of observational and inductive biases corresponding to the physical nature of the system through data preprocessing and search space restriction, AI Feynman was successful in recovering the first-order analytical form of this equation from lunar ephemerides data. However, this manual approach highlights a key limitation in its reliance on expert-driven coordinate system selection. We therefore propose an automated preprocessing extension to find the canonical coordinate system. Results demonstrate that targeted domain knowledge embedding enables symbolic regression to rediscover physical laws, but also highlight further challenges in constraining symbolic regression to derive physics equations when leveraging domain knowledge through tailored biases. |
| title | Rediscovering the Lunar Equation of the Centre with AI Feynman via Embedded Physical Biases |
| topic | Machine Learning Instrumentation and Methods for Astrophysics I.2.1 |
| url | https://arxiv.org/abs/2511.09979 |