Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model

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成果类型：

期刊论文

作者：

Chen, Xiangna;Liu, Feiyi;Deng, Weibing;Chen, Shiyang;Shen, Jianmin;...

通讯作者：

Liu, FY

作者机构：

[Yang, Chunbin; Liu, Feiyi; Deng, Weibing; Li, Wei; Chen, Xiangna; Liu, FY] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.

[Liu, Feiyi; Deng, Weibing; Li, Wei; Liu, FY] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.

[Liu, Feiyi; Papp, Gabor; Liu, FY] Eotv Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.

[Chen, Shiyang] Baoshan Univ, Sch Engn & Technol, Baoshan 678000, Peoples R China.

[Shen, Jianmin] Max Planck Inst Math Sci, D-04103 Leipzig, Germany.

通讯机构：

[Liu, FY ] C

Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.

Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.

Eotv Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.

语种：

英文

关键词：

Machine learning;Domain adversarial neural network;3D Potts model;Phase transitions;Critical phenomena

期刊：

Physica A-Statistical Mechanics and its Applications

ISSN：

0378-4371

年：

2024

卷：

637

页码：

129533

DOI：

10.1016/j.physa.2024.129533

基金类别：

CRediT authorship contribution statement Xiangna Chen: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Conceptualization. Feiyi Liu: Writing – review & editing, Supervision, Software, Methodology. Weibing Deng: Writing – review & editing, Supervision, acquisition, Conceptualization. Shiyang Chen: Writing – review & editing, Software. Jianmin Shen: Methodology, Conceptualization. Gábor Papp: Writing – review & editing, Validation, Software, acquisition. Wei Li: Writing – review

机构署名：

本校为第一且通讯机构

院系归属：

物理科学与技术学院

摘要：

Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional q -state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high -temperature (disordered) and low -temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations...

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Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model

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