Machine Learning Lie Structures & Applications to Physics
This work addresses a computational bottleneck in physics for researchers analyzing symmetry in physical systems, but it is incremental as it applies existing ML methods to a new domain.
The paper tackled the problem of computing tensor products and branching rules of irreducible representations in Lie algebras, achieving relative speed-ups of orders of magnitude compared to non-ML algorithms.
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.