We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symmetry-breaking phase transitions in considered systems irrespective of the universality class, order, and the presence of discrete or continuous degrees of freedom. No prior knowledge about the existence of a phase transition is required in the target system and its entire parameter space can be scanned with multiple histogram reweighting to discover one. We establish our approach in $q$-state Potts models and perform a calculation for the critical coupling and the critical exponents of the ${\varphi }^4$ scalar field theory using quantities derived from the neural network implementation. We view the machine learning algorithm as a mapping that associates each configuration across different systems to its corresponding phase and elaborate on implications for the discovery of unknown phase transitions.

中文翻译：

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将不同的相变映射到神经网络

我们通过卷积神经网络证明，在二维Ising模型中学习的特征具有足够的通用性，可以预测所考虑系统中对称破坏相变的结构，而与通用性类别，阶数和存在性无关离散或连续的自由度。在目标系统中不需要有关相变存在的先验知识，并且可以使用多个直方图重加权来扫描其整个参数空间以发现一个相变。我们建立我们的方法$q$状态Potts模型，并对其的临界耦合和临界指数进行计算 ${\varphi }^4$标量场理论使用从神经网络实现派生的数量。我们将机器学习算法视为一种映射，该映射将跨不同系统的每个配置与其对应的阶段相关联，并详细说明了发现未知相变的含义。