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
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 Science & Technology > Mathematics |
| SWORD Depositor: |
Preview
Available under License Creative Commons: Attribution International Public License 4.0.
Download (950kB) | Preview
Export
Downloads
Downloads per month over past year
Metadata
Metadata
MetadataAltmetric
CORE (COnnecting REpositories)
Actions (login required)
Actions (login required)
Admin Login