Twin-driven Optimization
Twin-Driven Optimization is a computational methodology that leverages real-time bidirectional data flows between a physical system and its digital twin to dynamically refine operational performance, distinguished by its closed-loop integration of simulation, analytics, and control mechanisms for continuous system improvement.
134
Publications
7.7K
Citations
480
Authors
174
Institutions
Publications per year
2017–2026
129
Parents
134
Jiewu Leng, Douxi Yan, Qiang Liu et al. · International Journal of Computer Integrated Manufacturing · 2019 · 183 citations
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174

Southeast University
Nanjing, China
6 h-index
15 publications
726 citations

Chongqing University
Chongqing, China
5 h-index
10 publications
806 citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
4 publications
183 citations
Nanyang Technological University
Singapore, Singapore
5 h-index
6 publications
563 citations
Guangdong University of Technology
Guangzhou, China
5 h-index
30 publications
8.3K citations
Tsinghua University
Beijing, China
4 h-index
11 publications
537 citations

Shanghai University
Shanghai, China
4 h-index
10 publications
371 citations
Jiangsu University of Science and Technology
Zhenjiang, China
4 h-index
15 publications
963 citations
1–8 of 174
8 publications · 839 citations
10
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5 publications · 313 citations
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4 publications · 288 citations
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3 publications · 140 citations
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3 publications · 255 citations
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