2019/05/27 by Silviu-Marian Udrescu, Max Tegmark · 5 voices · 144 citations
Computer Science · Physics and Astronomy · #cs.AI #cs.LG #hep-th #physics.comp-ph
published as Science Advances, 6:eaay2631, April 15, 2020 · 15 pages, 2 figs. Our code is available at https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression Database for benchmarking can be downloaded at https://space.mit.edu/home/tegmark/aifeynman.html
arxiv created 2020/04/15 · arxiv updated 2020/04/16
A core challenge for both physics and artificial intellicence (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 test set, we improve the state of the art success rate from 15% to 90%.