Hyper-reduction

Hyper-Reduction is a model reduction technique that belongs to the class of computational methods designed to accelerate reduced-order simulations by selectively sampling or approximating the nonlinear terms, distinguished by its explicit reduction of the computational cost associated with evaluating high-dimensional residual or force vectors through localized or sparse sampling strategies.

71

Publications

4.5K

Citations

163

Authors

89

Institutions

Publications per year

2017–2026

52

Authors

163

  1. CF
    Charbel Farhat

    Stanford University, United States

    9 publications · 1.5K citations

    9

    h-index

  2. DR
    David Ryckelynck

    Centre National de la Recherche Scientifique, France

    8 publications · 774 citations

    8

    h-index

  3. PA
    Philip Avery

    Stanford University, United States

    6 publications · 821 citations

    6

    h-index

  4. SF
    Stefania Fresca

    Politecnico di Milano, Italy

    5 publications · 380 citations

    5

    h-index

  5. AM
    Andrea Manzoni

    Politecnico di Milano, Italy

    5 publications · 355 citations

    5

    h-index

1–5 of 163

Venues

  1. 10

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  2. 9

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  3. Journal of Computational Physics

    4 publications · 455 citations

    4

    h-index

  4. SIAM Journal on Scientific Computing

    3 publications · 137 citations

    3

    h-index

  5. Journal of Scientific Computing

    3 publications · 115 citations

    3

    h-index

1–5