PT  - JOURNAL ARTICLE
AU  - Udrescu, Silviu-Marian
AU  - Tegmark, Max
TI  - AI Feynman: A physics-inspired method for symbolic regression
AID  - 10.1126/sciadv.aay2631
DP  - 2020 Apr 01
TA  - Science Advances
PG  - eaay2631
VI  - 6
IP  - 16
4099  - http://advances.sciencemag.org/content/6/16/eaay2631.short
4100  - http://advances.sciencemag.org/content/6/16/eaay2631.full
SO  - Sci Adv2020 Apr 01; 6
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%.