论文标题
Abelian仪表理论的机器学习阶段
Machine learning phases of an Abelian gauge theory
论文作者
论文摘要
三维$ u（1）$ u（1）$量子链接模型的相变，通过使用仅由一个输入层，两个神经元的一个隐藏层和一个输出层组成的有监督神经网络（NN）。进行NN训练时，未使用有关研究模型的信息。取而代之的是，两个人为的配置被视为训练集。有趣的是，获得的NN不仅准确地估算了临界点，而且还正确揭示了物理学。此处介绍的结果暗示，有监督的NN具有非常简单的体系结构，并且经过训练而没有研究模型的任何输入，可以高精度地识别目标相结构。
The phase transition of the two-dimensional $U(1)$ quantum link model on the triangular lattice is investigated by employing a supervised neural network (NN) consisting of only one input layer, one hidden layer of two neurons, and one output layer. No information on the studied model is used when the NN training is conducted. Instead, two artificially made configurations are considered as the training set. Interestingly, the obtained NN not only estimates the critical point accurately but also uncovers the physics correctly. The results presented here imply that a supervised NN, which has a very simple architecture and is trained without any input from the investigated model, can identify the targeted phase structure with high precision.
