Abstract: 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 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 is able to discover all of them, while previous publicly available software cracks at most 71.
