mlsanityData to 7 Oct 2026

Machine Learning Lie Structures & Applications to Physics

arXiv 2011.00871 ↗First posted 2 Nov 2020hep-th cs.LG hep-phcited by 13

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Abstract (arXiv)

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 are machine-learnable, and can achieve relative speed-ups of orders of magnitude in comparison to the non-ML algorithms.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Nov 2020.

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Citing papers per year: 2021: 2; 2022: 2; 2023: 6; 2024: 2; 2025: 1.

13 papers.

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  1. 2021Machine-Learning Mathematical Structurescited by 25
  2. 2023Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principlescited by 22
  3. 2021Machine learning a manifoldcited by 9
  4. 2023Discovering Sparse Representations of Lie Groups with Machine Learningcited by 8
  5. 2023Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6cited by 6
  6. 2023Oracle-Preserving Latent Flowscited by 5
  7. 2024A Triumvirate of AI Driven Theoretical Discoverycited by 5
  8. 2022Machine learning of the well known thingscited by 3
  9. 2022From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlookcited by 2
  10. 2023Black holes and the loss landscape in machine learningcited by 0
  11. 2023Learning to be Simplecited by 0
  12. 2024Optimal Potential Shaping on SE(3) via Neural ODEs on Lie Groupscited by 0
1 more
  1. 2025Deep Learning based discovery of Integrable Systemscited by 0