Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Wang, Yanyang;Li, Wei;Liu, Feiyi;Shen, Jianmin

通讯作者：

Li, W

作者机构：

[Liu, Feiyi; Li, W; Wang, Yanyang; Li, Wei; Shen, Jianmin] Cent China Normal Univ, Key Lab Quark & Lepton Phys MOE, Wuhan 430079, Peoples R China.

[Liu, Feiyi; Li, W; Wang, Yanyang; Li, Wei; Shen, Jianmin] Cent China Normal Univ, Inst Particle Phys, Wuhan 430079, Peoples R China.

[Liu, Feiyi] Eotvos Lorand Univ, Inst Phys, 1-A Pazmany P Setany, H-1117 Budapest, Hungary.

[Shen, Jianmin] Baoshan Univ, Coll Engn & Technol, Baoshan, Peoples R China.

通讯机构：

[Li, W ] 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.

语种：

英文

关键词：

machine learning;non-equilibrium phase transitions;branching annihilating random walks;convolutional neural networks;autoencoder

期刊：

Machine Learning: Science and Technology

ISSN：

2632-2153

年：

2024

卷：

期：

DOI：

10.1088/2632-2153/ad27e2

基金类别：

Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University [QLPL2022P01]; National Research Incubation Fund of Baoshan University [BYPY202216]; Fundamental Research Funds for Central Universities, China [CCNU19QN029]; National Natural Science Foundation of China [11505071, 61702207, 61873104]; The 111 Project [BP0820038]

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 (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 study, ML is applied to (1+1)-d, even offspring branching ann...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问