Data-driven Multiscale Modeling

Data-Driven Multiscale Modeling is a computational framework within multiscale solid mechanics that integrates empirical or simulated data across spatial and temporal scales to inform, refine, or construct constitutive models or homogenization procedures, distinguishing itself from traditional physics-based approaches by relying on data-informed rather than purely first-principles-derived relationships.

67

Publications

4.4K

Citations

242

Authors

152

Institutions

Publications per year

2017–2026

47

Authors

242

  1. SL
    Steven L. Brunton

    University of Washington, United States

    6 publications · 694 citations

    6

    h-index

  2. SR
    Surya R. Kalidindi

    Drexel University, United States

    4 publications · 248 citations

    4

    h-index

  3. JN
    J. Nathan Kutz

    University of Washington Applied Physics Laboratory, United States

    4 publications · 647 citations

    4

    h-index

  4. HH
    Heng Hu

    Wuhan University, China

    3 publications · 225 citations

    3

    h-index

  5. YL
    Yuying Liu

    University of Washington, United States

    3 publications · 92 citations

    3

    h-index

1–5 of 242

Venues

  1. 5

    h-index

  2. 2

    h-index

  3. Journal of the Mechanics and Physics of Solids

    2 publications · 381 citations

    2

    h-index

  4. Composite Structures

    2 publications · 126 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 37 citations

    2

    h-index

1–5