Universality class of machine learning for critical phenomena

Gaoke Hu; Yu Sun; Teng Liu; Yongwen Zhang; Maoxin Liu; Jingfang Fan; Wei Chen; Xiaosong Chen

doi:10.1007/s11433-023-2221-8

Universality class of machine learning for critical phenomena

Gaoke Hu
, Yu Sun
, Teng Liu
, Yongwen Zhang
, Maoxin Liu
, Jingfang Fan
, Wei Chen
, Xiaosong Chen

Beijing Normal University
Kunming University of Science and Technology
CAS - Institute of Process Engineering
University of Chinese Academy of Sciences

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

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.

Original language	English
Article number	120511
Journal	Science China: Physics, Mechanics and Astronomy
Volume	66
Issue number	12
DOIs	https://doi.org/10.1007/s11433-023-2221-8
State	Published - Dec 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023, Science China Press.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 12135003, and 12275020).

Funders	Funder number
National Natural Science Foundation of China	12135003, 12275020

Keywords

machine learning
percolation
universality class

Access to Document

10.1007/s11433-023-2221-8

Cite this

@article{6d52b627373b4feb84d3ee1c62e11043,

title = "Universality class of machine learning for critical phenomena",

abstract = "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.",

keywords = "machine learning, percolation, universality class",

author = "Gaoke Hu and Yu Sun and Teng Liu and Yongwen Zhang and Maoxin Liu and Jingfang Fan and Wei Chen and Xiaosong Chen",

note = "Publisher Copyright: {\textcopyright} 2023, Science China Press.",

year = "2023",

month = dec,

doi = "10.1007/s11433-023-2221-8",

language = "אנגלית",

volume = "66",

journal = "Science China: Physics, Mechanics and Astronomy",

issn = "1674-7348",

publisher = "Springer",

number = "12",

}

TY - JOUR

T1 - Universality class of machine learning for critical phenomena

AU - Hu, Gaoke

AU - Sun, Yu

AU - Liu, Teng

AU - Zhang, Yongwen

AU - Liu, Maoxin

AU - Fan, Jingfang

AU - Chen, Wei

AU - Chen, Xiaosong

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

KW - machine learning

KW - percolation

KW - universality class

UR - https://www.scopus.com/pages/publications/85177564213

U2 - 10.1007/s11433-023-2221-8

DO - 10.1007/s11433-023-2221-8

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:85177564213

SN - 1674-7348

VL - 66

JO - Science China: Physics, Mechanics and Astronomy

JF - Science China: Physics, Mechanics and Astronomy

IS - 12

M1 - 120511

ER -

Universality class of machine learning for critical phenomena

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this