Machine learning Lie structures & applications to physics

被引：12

作者：

Chen, Heng-Yu ^{[1
]}

He, Yang-Hui ^{[2
,3
,4
]}

Lal, Shailesh ^{[5
]}

Majumder, Suvajit ^{[2
]}

机构：

[1] Natl Taiwan Univ, Dept Phys, Taipei 10617, Taiwan

[2] City Univ London, Dept Math, London EC1V 0HB, England

[3] Univ Oxford, Merton Coll, Oxford OX1 4JD, England

[4] Nankai Univ, Sch Phys, Tianjin 300071, Peoples R China

[5] Univ Porto, Fac Ciencias, 687 Rua Campo Alegre, P-4169007 Porto, Portugal

来源：

PHYSICS LETTERS B | 2021年 / 817卷

关键词：

D O I：

10.1016/j.physletb.2021.136297

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

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. (C) 2021 The Author(s). Published by Elsevier B.V.

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页数：5

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