Published June 2021
| Version v1
Journal article
Machine learning Lie structures & applications to physics
- 1. Department of Physics, National Taiwan University, Taipei 10617 (China)
- 2. School of Physics, NanKai University, Tianjin, 300071 (China)
- 3. Merton College, University of Oxford, OX1 4JD (United Kingdom)
- 4. Department of Mathematics, City, University of London, London EC1V0HB (United Kingdom)
- 5. Faculdade de Ciencias, Universidade do Porto, 687 Rua do Campo Alegre, Porto 4169-007 (Portugal)
Description
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.
Availability note (English)
Available from http://dx.doi.org/10.1016/j.physletb.2021.136297Additional details
Identifiers
- DOI
- 10.1016/j.physletb.2021.136297;
- PII
- S0370269321002379;
Publishing Information
- Journal Title
- Physics Letters. Section B
- Journal Volume
- 817
- Journal Page Range
- vp.
- ISSN
- 0370-2693
- CODEN
- PYLBAJ
INIS
- Country of Publication
- Netherlands
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 54011391
- Subject category
- S97: MATHEMATICAL METHODS AND COMPUTING;
- Descriptors DEI
- BRANCHING RATIO; CALCULATION METHODS; IRREDUCIBLE REPRESENTATIONS; LIE GROUPS; MACHINE LEARNING; TENSORS
- Descriptors DEC
- ALGORITHMS; ARTIFICIAL INTELLIGENCE; DIMENSIONLESS NUMBERS; LEARNING; MATHEMATICAL LOGIC; SYMMETRY GROUPS
Optional Information
- Copyright
- Copyright (c) 2021 The Author(s). Published by Elsevier B.V.