RT Journal Article
SR Electronic
T1 AI Feynman: A physics-inspired method for symbolic regression
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaay2631
DO 10.1126/sciadv.aay2631
VO 6
IS 16
A1 Udrescu, Silviu-Marian
A1 Tegmark, Max
YR 2020
UL http://advances.sciencemag.org/content/6/16/eaay2631.abstract
AB 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%.