Detection of Phase Transition via Convolutional Neural Networks

被引：128

作者：

Tanaka, Akinori ^{[1
]}

Tomiya, Akio ^{[2
,3
]}

机构：

[1] RIKEN, Interdisciplinary Math & Computat Collaborat Team, Wako, Saitama 3510198, Japan

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

[3] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China

来源：

JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN | 2017年 / 86卷 / 06期

关键词：

D O I：

10.7566/JPSJ.86.063001

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

A convolutional neural network (CNN) is designed to study correlation between the temperature and the spin configuration of the two-dimensional Ising model. Our CNN is able to find the characteristic feature of the phase transition without prior knowledge. Also a novel order parameter on the basis of the CNN is introduced to identify the location of the critical temperature; the result is found to be consistent with the exact value.

引用

页数：4

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