Similarity-Based Equational Inference in Physics
  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.
