TY - JOUR
T1 - AI Feynman: A physics-inspired method for symbolic regression
JF - Science Advances
JO - Sci Adv
DO - 10.1126/sciadv.aay2631
VL - 6
IS - 16
SP - eaay2631
AU - Udrescu, Silviu-Marian
AU - Tegmark, Max
Y1 - 2020/04/01
UR - http://advances.sciencemag.org/content/6/16/eaay2631.abstract
N2 - 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%.
ER -