Universality class of machine learning for critical phenomena  

作　　者：Gaoke Hu Yu Sun Teng Liu Yongwen Zhang Maoxin Liu Jingfang Fan Wei Chen Xiaosong Chen 

机构地区：[1]School of Systems Science and Institute of Nonequilibrium Systems,Beijing Normal University,Beijing 100875,China [2]Data Science Research Center,Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China [3]State Key Laboratory of Multiphase Complex Systems,Institute of Process Engineering,Chinese Academy of Sciences,Beijing 100190,China [4]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Science China(Physics,Mechanics & Astronomy)》2023年第12期63-70,共8页中国科学：物理学、力学、天文学（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.12135003,and 12275020)。

摘　　要：Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques.Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different NNs.Based on this finding,we proposed the universality class of machine learning for critical phenomena.Furthermore,we investigated and discussed the NNs of different universality classes.Our research contributes to machine learning by relating the NNs with the universality class.

关 键 词：universality class machine learning PERCOLATION 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] O552.6[自动化与计算机技术—控制科学与工程]