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.

引用

下载

页数：5

共 50 条

[1] Machine learning applications in nuclear physics
He WanBing
He JunJie
Wang Rui
Ma YuGang
SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA, 2022, 52 (05)
[2] Machine and deep learning applications in particle physics
Bourilkov, Dimitri
INTERNATIONAL JOURNAL OF MODERN PHYSICS A, 2019, 34 (35):
[3] Hilbert series, machine learning, and applications to physics
Bao, Jiakang
He, Yang-Hui
Hirst, Edward
Hofscheier, Johannes
Kasprzyk, Alexander
Majumder, Suvajit
PHYSICS LETTERS B, 2022, 827
[4] Revolutionizing physics: a comprehensive survey of machine learning applications
Suresh, Rahul
Bishnoi, Hardik
Kuklin, Artem V.
Parikh, Atharva
Molokeev, Maxim
Harinarayanan, R.
Gharat, Sarvesh
Hiba, P.
FRONTIERS IN PHYSICS, 2024, 12
[5] Special topic: Applications of the machine learning in nuclear physics
Li, QingFeng
Ma, YuGang
SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA, 2022, 52 (05)
[6] The Integration of AI and Machine Learning in Medical Physics Applications
Solberg, Timothy
Kearney, V.
Chan, M.
Cardenas, C.
MEDICAL PHYSICS, 2019, 46 (06) : E343 - E343
[7] Applications of machine learning in relativistic heavy ion physics
Zhou Meng
Luo YiQun
Song HuiChao
SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA, 2022, 52 (05)
[8] APPLICATIONS OF LIE SERIES IN ATOMIC PHYSICS
CAP, FF
WEIL, JW
ATOMIC ENERGY REVIEW, 1970, 8 (03) : 621 - &
[9] Applications of Machine Learning to Foundation Design for Transportation Structures
Wu, Hongyang
Aguilar, Victor
Montgomery, Jack
TRANSPORTATION RESEARCH RECORD, 2022,
[10] Machine Learning Applications in Nondestructive Testing of Concrete Structures
Algernon, Daniel
Muench, Ingo
Muller, Aurelia
Thurnherr, Claudia
EUROPEAN ASSOCIATION ON QUALITY CONTROL OF BRIDGES AND STRUCTURES, EUROSTRUCT 2023, VOL 6, ISS 5, 2023, : 239 - 250

← 1 2 3 4 5 →