  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%.
