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

Authors

480

  1. QL
    Qiang Liu

    Guangdong University of Technology, China

    4 publications · 1.3K citations

    4

    h-index

  2. JL
    Jiewu Leng

    Guangdong University of Technology, China

    4 publications · 1.3K citations

    4

    h-index

  3. XC
    Xin Chen

    Guangdong University of Technology, China

    3 publications · 848 citations

    3

    h-index

  4. YG
    Yinping Gao

    Shanghai Maritime University, China

    3 publications · 143 citations

    3

    h-index

  5. DC
    Daofang Chang

    Shanghai Maritime University, China

    3 publications · 143 citations

    3

    h-index

1–5 of 480

Venues

  1. Journal of Manufacturing Systems

    8 publications · 839 citations

    10

    h-index

  2. Robotics and Computer-Integrated Manufacturing

    5 publications · 313 citations

    7

    h-index

  3. Applied Energy

    4 publications · 288 citations

    4

    h-index

  4. Journal of Intelligent Manufacturing

    3 publications · 140 citations

    4

    h-index

  5. 4

    h-index

1–5