City Research Online

Machine learning Lie structures & applications to physics

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


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 (
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QC Physics
Departments: School of Science & Technology > Mathematics
SWORD Depositor:
[thumbnail of scoap3-fulltext.pdf]
Text - Published Version
Available under License Creative Commons: Attribution International Public License 4.0.

Download (950kB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login