Combining data and theory for derivable scientific discovery with AI-Descartes.

ABSTRACT: Scientists aim to discover meaningful formulae that accurately describe experimental data. Mathematical models of natural phenomena can be manually created from domain knowledge and fitted to data, or, in contrast, created automatically from large datasets with machine-learning algorithms. The problem of incorporating prior knowledge expressed as constraints on the functional form of a learned model has been studied before, while finding models that are consistent with prior knowledge expressed via general logical axioms is an open problem. We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression. We demonstrate these concepts for Kepler's third law of planetary

SUBMITTER: Cornelio C 

PROVIDER: S-EPMC10097814 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature