期刊缩写:
DT
ISSN:
print: 2752-5783
研究领域:
digital twin technologies-
创刊年份:
2021年
Gold OA文章占比:
0.00%
原创研究文献占比:
0.00%
期刊官网:
期刊介绍英文:
Digital Twin is a rapid multidisciplinary open access publishing platform for state-of-the-art, basic, scientific and applied research on digital twin technologies. Digital Twin covers all areas related digital twin technologies, including broad fields such as smart manufacturing, civil and industrial engineering, healthcare, agriculture, and many others. The platform is open to submissions from researchers, practitioners and experts, and all articles will benefit from open peer review.  The aim of Digital Twin is to advance the state-of-the-art in digital twin research and encourage innovation by highlighting efficient, robust and sustainable multidisciplinary applications across a variety of fields. Challenges can be addressed using theoretical, methodological, and technological approaches. The scope of Digital Twin includes, but is not limited to, the following areas:  ● Digital twin concepts, architecture, and frameworks ● Digital twin theory and method ● Digital twin key technologies and tools ● Digital twin applications and case studies ● Digital twin implementation ● Digital twin services ● Digital twin security ● Digital twin standards Digital twin also focuses on applications within and across broad sectors including: ● Smart manufacturing ● Aviation and aerospace ● Smart cities and construction ● Healthcare and medicine ● Robotics ● Shipping, vehicles and railways ● Industrial engineering and engineering management ● Agriculture ● Mining ● Power, energy and environment Digital Twin features a range of article types including research articles, case studies, method articles, study protocols, software tools, systematic reviews, data notes, brief reports, and opinion articles.
发文信息
历年影响因子
历年发表
投稿信息
出版语言:
English
出版国家(地区):
China
出版商:
Taylor & Francis Group
编辑部地址:
Beihang University

Digital Twin - 最新文献

Digital twin-based modeling of natural gas leakage and dispersion in urban utility tunnels

Pub Date : 2024-07-17 DOI: 10.12688/digitaltwin.17963.1 Jitao Cai, Jiansong Wu, Yanzhu Hu, Ziqi Han, Yuefei Li, Ming Fu, Xiaofu Zou, Xin Wang

Data-driven modeling in digital twin for power system anomaly detection

Pub Date : 2024-04-11 DOI: 10.12688/digitaltwin.17734.1 Xin Shi, Fang Fang, Robert Qiu

Is it possible to develop a digital twin for noise monitoring in manufacturing?

Pub Date : 2024-03-28 DOI: 10.12688/digitaltwin.17931.1 Li Yi, Patrick Ruediger-Flore, Ali Karnoub, Jan Mertes, Moritz Glatt, J. Aurich
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