论文标题
具有持续同源性的可解释的阶段检测和分类
Interpretable Phase Detection and Classification with Persistent Homology
论文作者
论文摘要
我们将持久的同源性应用于发现和表征相变的任务，使用统计物理学的晶格自旋模型用于工作示例。持续图像为执行统计任务的同源数据提供了有用的表示。为了识别相变，这些图像上的简单逻辑回归足以满足我们考虑的模型，然后从回归的权重中读取可解释的顺序参数。磁化，挫败感和涡流 - 抗杀伤结构被确定为表征相变的相关特征。
We apply persistent homology to the task of discovering and characterizing phase transitions, using lattice spin models from statistical physics for working examples. Persistence images provide a useful representation of the homological data for conducting statistical tasks. To identify the phase transitions, a simple logistic regression on these images is sufficient for the models we consider, and interpretable order parameters are then read from the weights of the regression. Magnetization, frustration and vortex-antivortex structure are identified as relevant features for characterizing phase transitions.
