Celestial Machine Learning: Discovering the Planarity, Heliocentricity, and Orbital Equation of Mars with AI Feynman
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Abstract (arXiv)
Can a machine or algorithm discover or learn the elliptical orbit of Mars from astronomical sightings alone? Johannes Kepler required two paradigm shifts to discover his First Law regarding the elliptical orbit of Mars. Firstly, a shift from the geocentric to the heliocentric frame of reference. Secondly, the reduction of the orbit of Mars from a three- to a two-dimensional space. We extend AI Feynman, a physics-inspired tool for symbolic regression, to discover the heliocentricity and planarity of Mars' orbit and emulate his discovery of Kepler's first law.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 20 references that are in the map. First posted on arXiv 19 Dec 2023.
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4 of 20 references.
References · 4
- 2019AI Feynman: a Physics-Inspired Method for Symbolic Regressioncited by 548
- 2022Interpretable Scientific Discovery with Symbolic Regression: A Reviewcited by 105
- 2020Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningcited by 2
- 2023Celestial Machine Learning: From Data to Mars and Beyond with AI Feynmancited by 2
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