Similarity-Based Equational Inference in Physics
Abstract
Automating the derivation of published results is a challenge, in part due to the informal use of mathematics by physicists, compared to that of mathematicians. Following demand, we describe a method for converting informal hand-written derivations into datasets, and present an example dataset crafted from a contemporary result in condensed matter. We define an equation reconstruction task completed by rederiving an unknown intermediate equation posed as a state, taken from three consecutive equational states within a derivation. Derivation automation is achieved by applying string-based CAS-reliant actions to states, which mimic mathematical operations and induce state transitions. We implement a symbolic similarity-based heuristic search to solve the equation reconstruction task as an early step towards multi-hop equational inference in physics.
Cite
@article{arxiv.2103.13496,
title = {Similarity-Based Equational Inference in Physics},
author = {Jordan Meadows and André Freitas},
journal= {arXiv preprint arXiv:2103.13496},
year = {2021}
}