Derivation in physics, in the form of derivation reconstruction of published results , is expensive and difficult to automate, not least because the use of mathematics by physicists is less formal than that of mathematicians. Following demand for informal mathematical datasets , we describe a dataset creation method where we consider a derivation agent as a finite state machine which exists in equational states represented by strings, where transitions can occur through a combination of string operations that mimic mathematics, and defined computer algebra operations. We present the novel dataset PhysAI-DS1 generated by this method, which consists of a curated derivation of a contemporary condensed matter physics result reconstructed using a computer algebra system . We define an equation reconstruction task based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing an unknown intermediate state equivalent to one-hop inference, extensible to the multi-hop case. We present a symbolic similarity-based heuristic approach to solve an equation reconstruction task on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit. Informal derivation comprehension of contemporary results is an important step towards the comprehension and automation of modern physicsreasoners .
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 .
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Derivation in physics, in the form of derivation reconstruction
Automating the derivation
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, is expensive and difficult to automate, not least because the
is a challenge, in part due to the informal
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is less formal than
, compared to
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for informal mathematical datasets
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dataset creation method where we consider a derivation agent as a finite state machine which exists in equational states represented by strings, where transitions can occur through a combination of string operations that mimic mathematics, and defined computer algebra operations. We present the novel dataset PhysAI-DS1 generated by this method, which consists of a curated derivation of a contemporary condensed matter physics result reconstructed using a computer algebra system
method for converting informal hand-written derivations into datasets, and present an example dataset crafted from a contemporary result in condensed matter
R
based on formulating derivation segments as basic units of non-trivial state sequences, with the goal of reconstructing
completed by rederiving
R
state equivalent to one-hop inference, extensible to the multi-hop case. We present
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
R
approach to solve an
search to solve the
R
on the PhysAI-DS1 dataset, which employs a set of actions, a knowledge base of symbols and equations, and a computer algebra system, to reconstruct an unknown intermediate state within a sequence of three equational states, grouped together as a derivation unit. Informal derivation comprehension of contemporary results is an important step towards the comprehension and automation of modern physicsreasoners
as an early step towards multi-hop equational inference in physics