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Machine learning Lie structures & applications to physics

Chen, H-Y., He, Y. ORCID: 0000-0002-0787-8380, Lal, S. and Majumder, S. (2021). Machine learning Lie structures & applications to physics. Physics Letters B, 817, p. 136297. doi: 10.1016/j.physletb.2021.136297

Abstract

Classical and exceptional Lie algebras and their representations are among the most important tools in the analysis of symmetry in physical systems. In this letter we show how the computation of tensor products and branching rules of irreducible representations is machine-learnable, and can achieve relative speed-ups of orders of magnitude in comparison to the non-ML algorithms.

Publication Type: Article
Additional Information: © 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QC Physics
Departments: School of Mathematics, Computer Science & Engineering > Mathematics
Date Deposited: 06 May 2021 10:09
URI: https://openaccess.city.ac.uk/id/eprint/26089
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