AI Feynman: A physics-inspired method for symbolic regression
Name
eaay2631.full.pdf
Description
Published version
Size
461.91 KB
Format
Unknown
Checksum (MD5)
9b4cdb716d8f82c7bb2bad2fdfabb67d
Author(s)
Udrescu, Silviu-Marian
Tegmark, Max Erik
Date Issued
2020
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%.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Center for Brains, Minds, and Machines
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/SCIADV.AAY2631
