SynPAT: A System for Generating Synthetic Physical Theories with Data

Jonathan Lenchner, Karan Srivastava, Joao Goncalves, Lior Horesh

Abstract: Machine-assisted methods for discovering new physical laws of nature, starting from a given background theory and data, have recently emerged, and seem to hold the promise of someday advancing our understanding of the physical world. To address these needs, we have developed SynPAT, a system for generating synthetic physical theories comprising (i) a set of consistent axioms, (ii) a symbolic expression that is a consequence of the axioms and the challenge to be discovered, and (iii) noisy data that approximately match the consequence. We also generate theories that do not correctly predict the consequence. We give a detailed description of the inner workings of SynPAT and its various capabilities. We also report on our benchmarking of several open-source symbolic regression systems using our generated theories and data.

External IDs:dblp:journals/corr/abs-2505-00878