Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. One common approach is to manually create mathematical models of natural phenomena using domain knowledge, then fit these models to data. In contrast, machine-learning algorithms automate the construction of accurate data-driven models while consuming large amounts of data. Ensuring that such models are consistent with existing knowledge is an open problem. We develop a method for combining logical reasoning with symbolic regression, enabling principled derivations of models of natural phenomena.
Scientists have long aimed to discover meaningful formulae which accurately describe experimental data. One common approach is to manually create mathematical models of natural phenomena using domain knowledge, then fit these models to data. In contrast, machine-learning algorithms automate the construction of accurate data-driven models while consuming large amounts of data. Ensuring that such models are consistent with existing knowledge is an open problem. We develop a method for combining logical reasoning with symbolic regression, enabling principled derivations of models of natural phenomena.