Experimental Data Assimilation

Experimental Data Assimilation is a computational framework in inverse problems in mechanics that integrates experimental measurements with numerical models to optimally estimate system states or parameters by minimizing discrepancies between observed data and model predictions.

41

Publications

1.3K

Citations

177

Authors

86

Institutions

Publications per year

2017–2026

7

Authors

177

  1. AM
    Alexandre M. Bayen

    University of California, Berkeley, United States

    3 publications · 104 citations

    3

    h-index

  2. IS
    Issam S. Strub

    University of California, Berkeley, United States

    2 publications · 41 citations

    2

    h-index

  3. DG
    Dan Gabriel Cacuci

    University of South Carolina, United States

    2 publications · 95 citations

    2

    h-index

  4. MK
    Masaru Kunii

    Meteorological Research Institute

    2 publications · 52 citations

    2

    h-index

  5. KS
    Kazuo Saito

    Meteorological Research Institute

    2 publications · 52 citations

    2

    h-index

1–5 of 177

Venues

  1. Monthly Weather Review

    5 publications · 131 citations

    5

    h-index

  2. Journal of Geophysical Research Atmospheres

    JGR · 4 publications · 151 citations

    4

    h-index

  3. 4

    h-index

  4. 2

    h-index

  5. Tellus A Dynamic Meteorology and Oceanography

    2 publications · 34 citations

    2

    h-index

1–5