Data-driven Modeling

Data-Driven Modeling is a computational modeling approach that belongs to the class of modeling methodologies in which the structure and parameters of the model are derived primarily from empirical data rather than from first-principles physics or theoretical assumptions.

1.4K

Publications

67.5K

Citations

5.3K

Authors

1.6K

Institutions

Publications per year

2017–2026

965

Authors

5.3K

  1. QC
    Qing Chang

    Stony Brook University, United States

    7 publications · 340 citations

    7

    h-index

  2. FC
    Francisco Chinesta

    Arts et Métiers, France

    7 publications · 503 citations

    7

    h-index

  3. FV
    Felipe Viana

    University of Central Florida, United States

    6 publications · 291 citations

    6

    h-index

  4. JY
    Juliana Y. Leung

    University of Alberta, Canada

    6 publications · 118 citations

    6

    h-index

  5. SL
    Steven L. Brunton

    University of Washington, United States

    6 publications · 570 citations

    6

    h-index

1–5 of 5.3K

Venues

  1. arXiv (Cornell University)

    Repository · 36 publications · 1.3K citations

    20

    h-index

  2. Industrial & Engineering Chemistry Research

    20 publications · 587 citations

    17

    h-index

  3. IEEE Access

    15 publications · 558 citations

    13

    h-index

  4. IEEE Transactions on Industrial Informatics

    13 publications · 984 citations

    13

    h-index

  5. 11

    h-index

1–5