Journal
SCIENCE ADVANCES
Volume 6, Issue 16, Pages -Publisher
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aay2631
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Funding
- Casey and Family Foundation
- Ethics and Governance of AI Fund
- Foundational Questions Institute
- Rothberg Family Fund for Cognitive Science
- Templeton World Charity Foundation Inc.
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A core challenge for both physics and artificial intelligence (Al) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult physics-based test set, we improve the state-of-the-art success rate from 15 to 90%.
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