Data-driven Aeroelastic Modeling

Data-Driven Aeroelastic Modeling is a computational modeling approach within aeroelastic response prediction that, unlike traditional physics-based methods, leverages empirical data and statistical learning techniques to directly identify or reconstruct the dynamic aeroelastic system behavior without explicit formulation of underlying physical equations.

20

Publications

902

Citations

66

Authors

31

Institutions

Publications per year

2017–2026

16

Authors

66

  1. SL
    Steven L. Brunton

    University of Washington, United States

    3 publications · 137 citations

    3

    h-index

  2. YW
    Yi Wang

    University of South Carolina, United States

    2 publications · 32 citations

    2

    h-index

  3. WZ
    Weiwei Zhang

    Northwestern Polytechnical University, China

    2 publications · 379 citations

    2

    h-index

  4. JK
    Jiaqing Kou

    Northwestern Polytechnical University, China

    2 publications · 379 citations

    2

    h-index

  5. KP
    Kapil Pant

    CFD Research Corporation (United States), United States

    2 publications · 32 citations

    2

    h-index

1–5 of 66

Venues

  1. AIAA Journal

    3 publications · 54 citations

    3

    h-index

  2. Journal of Fluids and Structures

    1 publications · 37 citations

    1

    h-index

  3. Theoretical and Computational Fluid Dynamics

    1 publications · 73 citations

    1

    h-index

  4. Energy and Buildings

    1 publications · 23 citations

    1

    h-index

  5. Aerobiologia

    1 publications · 22 citations

    1

    h-index

1–5