HEP-THMATH-PHMLJan 23, 2022

Machine Learning Symmetry

arXiv:2201.09345v21 citations
AI Analysis

This is an incremental review paper for researchers in theoretical physics and mathematics, consolidating prior work without tackling a new problem.

The paper reviews recent applications of neural networks to machine learning aspects of conformal field theory and Lie algebra representation theory, summarizing existing research without presenting new results.

We review recent work in machine learning aspects of conformal field theory and Lie algebra representation theory using neural networks.

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The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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