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Compositional Scientific Computing with Catlab and SemanticModels

Authors :
Halter, Micah
Patterson, Evan
Baas, Andrew
Fairbanks, James
Publication Year :
2020

Abstract

Scientific computing is currently performed by writing domain specific modeling frameworks for solving special classes of mathematical problems. Since applied category theory provides abstract reasoning machinery for describing and analyzing diverse areas of math, it is a natural platform for building generic and reusable software components for scientific computing. We present Catlab.jl, which provides the category-theoretic infrastructure for this project, together with SemanticModels.jl, which leverages this infrastructure for particular modeling tasks. This approach enhances and automates scientific computing workflows by applying recent advances in mathematical modeling of interconnected systems as cospan algebras.<br />Comment: 3 pages, 6 figures, Applied Category Theory 2020 conference

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2005.04831
Document Type :
Working Paper