论文标题
通过机器学习降低动力系统的维度：自动生成最佳的广泛变量及其时间进化图
Dimensional Reduction of Dynamical Systems by Machine Learning: Automatic Generation of the Optimum Extensive Variables and Their Time-Evolution Map
论文作者
论文摘要
提出了一个框架来生成一个现象学模型，该模型使用机器学习提取具有较大自由度的动力系统（DS）的本质。对于给定的显微镜DS，最佳转换对少数宏观变量，预计将是广泛的，并且会同时识别变量的时间演变规则。该方法的实用性通过其应用于三态POTTS模型的非平衡松弛。
A framework is proposed to generate a phenomenological model that extracts the essence of a dynamical system (DS) with large degrees of freedom using machine learning. For a given microscopic DS, the optimum transformation to a small number of macroscopic variables, which is expected to be extensive, and the rule of time evolution that the variables obey are simultaneously identified. The utility of this method is demonstrated through its application to the nonequilibrium relaxation of the three-state Potts model.
