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arXiv · 1905.11481

AI Feynman: a Physics-Inspired Method for Symbolic Regression

Silviu-Marian Udrescu·Max Tegmark
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Abstract

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%.

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Silviu-Marian Udrescu, Max Tegmark. 2019-05-27. AI Feynman: a Physics-Inspired Method for Symbolic Regression. https://arxiv.org/abs/1905.11481

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