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AI Feynman: A physics-inspired method for symbolic regression
Name
eaay2631.full.pdf
Description
Published version
Size
461.91 KB
Format
Adobe PDF
Checksum (MD5)
9b4cdb716d8f82c7bb2bad2fdfabb67d
Author(s)
Udrescu, Silviu-Marian
Tegmark, Max
Journal
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Version
Final published version
Abstract
© 2020 The Authors. A core challenge for both physics and artificial intelligence (AI) 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%.
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1126/SCIADV.AAY2631
