论文标题
机器学习谎言结构和物理应用
Machine Learning Lie Structures & Applications to Physics
论文作者
论文摘要
古典和特殊的谎言代数及其表示是分析物理系统对称性的最重要工具之一。在这封信中，我们展示了张量产品的计算和不可约表示的分支规则是可以机器切实可行的，并且与非ML算法相比，可以实现相对的数量级的相对加速。
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.
