(203b) Machine Learning + Automated Reasoning for Theory Discovery
AIChE Annual Meeting
Monday, November 8, 2021 - 3:45pm to 4:00pm
We propose a computational framework to discover and derive scientific theories, by integrating two typically distinct areas of computer science: machine learning and automated reasoning. Symbolic regression generates equations that empirically match experimental data. Top equations become "hypotheses" or "conjectures" to be proved or disproved by an automated theorem prover, starting from a set of "axioms" describing the environment under study. We demonstrate this framework by rediscovering and deriving Keplerâs Third Law from astronomical observations, and by rediscovering and deriving Langmuir adsorption from experimental measurements.