期刊论文

Li, W

英文

Machine Learning: Science and Technology

2632-2153

2024

5

1

015033

10.1088/2632-2153/ad27e2

This study was supported in part by the Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University (Grant No. QLPL2022P01), the National Research Incubation Fund of Baoshan University (BYPY202216), and the Fundamental Research Funds for Central Universities, China (Grant No. CCNU19QN029), National Natural Science Foundation of China (Grant Nos. 11505071, 61702207, and 61873104), and 111 Project (Grant No. BP0820038). We would like to thank Editage ( www.editage.cn ) for English language editing.

本校为第一且通讯机构

Abstract Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this s...