PySR
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| Initial release | September 14, 2020 |
|---|---|
| Repository | github |
| Written in | Python, Julia |
| Engine | |
| Platform | Linux, macOS, Windows |
| Type | Machine learning library |
| License | Apache License 2.0 |
| Website | astroautomata |
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PySR is a free and open-source software library for symbolic regression in Python and Julia.[1][2][3] One of the stated aims of the software is to "[develop] an open-source symbolic regression tool as efficient as [proprietary software], while also exposing a configurable python interface." The library has been used to discover new scientific models in several different fields,[4] including international economics,[5] the structure of dark matter halos,[6] and for modeling Large Hadron Collider data.[7]
PySR placed 2nd in the synthetic track of the 2022 Symbolic Regression competition ("SRBench") competition at GECCO conference.[8]
See also[edit]
References[edit]
- ↑ Cranmer, Miles (2022-10-18), PySR: High-Performance Symbolic Regression in Python, retrieved 2022-10-23
- ↑ Cranmer, Miles; Sanchez-Gonzalez, Alvaro; Battaglia, Peter; Xu, Rui; Cranmer, Kyle; Spergel, David; Ho, Shirley (2020-12-06). "Discovering symbolic models from deep learning with inductive biases". Proceedings of the 34th International Conference on Neural Information Processing Systems. NIPS'20. Red Hook, NY, USA: Curran Associates Inc.: 17429–17442. ISBN 978-1-7138-2954-6.
- ↑ Wood, Charlie (2022-05-10). "Powerful 'Machine Scientists' Distill the Laws of Physics From Raw Data". Quanta Magazine. Retrieved 2022-10-23.
- ↑ "Research - PySR". astroautomata.com. Retrieved 2022-10-23. Unknown parameter
|url-status=ignored (help) - ↑ Verstyuk, Sergiy; Douglas, Michael R. (2022-03-08). "Machine Learning the Gravity Equation for International Trade". SSRN. Rochester, NY.
- ↑ Shao, Helen; Villaescusa-Navarro, Francisco; Genel, Shy; Spergel, David N.; Anglés-Alcázar, Daniel; Hernquist, Lars; Davé, Romeel; Narayanan, Desika; Contardo, Gabriella; Vogelsberger, Mark (2022-03-08). "Finding Universal Relations in Subhalo Properties with Artificial Intelligence". The Astrophysical Journal. 927 (1). doi:10.3847/1538-4357/ac4d30/meta. ISSN 0004-637X.
- ↑ Butter, Anja; Plehn, Tilman; Soybelman, Nathalie; Brehmer, Johann (2021-11-20). "Back to the Formula -- LHC Edition". SciPost.
- ↑ Michael Kommenda; William La Cava; Maimuna Majumder; Fabricio Olivetti de França; Marco Virgolin (2022-07-22). "SRBench Competition 2022: Interpretable Symbolic Regression for Data Science".
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