Machine Learning Lie Structures Applications to Physics

11/02/2020

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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.

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Machine Learning Lie Structures Applications to Physics

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Machine Learning Lie Structures Applications to Physics

Related Research

Discovering Sparse Representations of Lie Groups with Machine Learning

Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6

Scalars are universal: Gauge-equivariant machine learning, structured like classical physics

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Lie Access Neural Turing Machine

Accelerating Physics Simulations with TPUs: An Inundation Modeling Example

Redatuming physical systems using symmetric autoencoders