论文标题
使用无监督的学习技术，在沮丧的Ising模型中对阶段行为的全球探索
Global exploration of phase behavior in frustrated Ising models using unsupervised learning techniques
论文作者
论文摘要
我们将一组机器学习（ML）技术应用于对两个具有竞争相互作用的沮丧2D ISING模型的相图。基于针对随机系统参数生成的原始蒙特卡洛自旋配置，我们应用主组件分析（PCA）和自动编码器来实现降低性降低，然后使用DBSCAN方法和支持矢量机分类器聚类来构建两个模型中不同阶段之间的过渡线。结果与可用的精确解决方案非常吻合，即使仅包含1400个旋转配置的数据集，自动编码器也会导致定量估计值。此外，结果表明，优化自动编码器潜在空间的结构与两个系统的物理特征之间存在关系。这表明，在\ emph {先验}理论洞察力不可用的情况下，使用的方法可用于感知物理系统的基本属性。
We apply a set of machine-learning (ML) techniques for the global exploration of the phase diagrams of two frustrated 2D Ising models with competing interactions. Based on raw Monte Carlo spin configurations generated for random system parameters, we apply principal-component analysis (PCA) and auto-encoders to achieve dimensionality reduction, followed by clustering using the DBSCAN method and a support-vector machine classifier to construct the transition lines between the distinct phases in both models. The results are in very good agreement with available exact solutions, with the auto-encoders leading to quantitatively superior estimates, even for a data set containing only 1400 spin configurations. In addition, the results suggest the existence of a relationship between the structure of the optimized auto-encoder latent space and physical characteristics of both systems. This indicates that the employed approach can be useful in perceiving fundamental properties of physical systems in situations where \emph{a priori} theoretical insight is unavailable.
