AI Feynman: a Physics-Inspired Method for Symbolic Regression

Autor:	Udrescu, Silviu-Marian, Tegmark, Max
Rok vydání:	2019
Předmět:	Physics - Computational Physics Computer Science - Artificial Intelligence Computer Science - Machine Learning High Energy Physics - Theory
Zdroj:	Science Advances, 6:eaay2631, April 15, 2020
Druh dokumentu:	Working Paper
Popis:	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%. Comment: 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
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.11481 Zobrazit plný text záznamu View this record from Arxiv