
A Simple ReDerivation of Onsager's Solution of the 2D Ising Model using Experimental Mathematics
In this case study, we illustrate the great potential of experimental ma...
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Computer Algebra in R with caracas
The capability of R to do symbolic mathematics is enhanced by the caraca...
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FiniteFlow: multivariate functional reconstruction using finite fields and dataflow graphs
Complex algebraic calculations can be performed by reconstructing analyt...
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Systematic Generation of Algorithms for Iterative Methods
The FLAME methodology makes it possible to derive provably correct algor...
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In Praise of Sequence (Co)Algebra and its implementation in Haskell
What is Sequence Algebra? This is a question that any teacher or student...
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Unification and combination of iterative insertion strategies with onestep traversals
Motivated by an ongoing project on the computer aided derivation of mult...
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Expectationbased Minimalist Grammars
Expectationbased Minimalist Grammars (eMGs) are simplified versions of...
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SimilarityBased Equational Inference in Physics
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 PhysAIDS1 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 nontrivial state sequences, with the goal of reconstructing an unknown intermediate state equivalent to onehop inference, extensible to the multihop case. We present a symbolic similaritybased heuristic approach to solve an equation reconstruction task on the PhysAIDS1 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 physics reasoners.
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