Snapshot-based Model Reduction

Snapshot-Based Model Reduction is a model reduction technique that constructs a reduced-order model by extracting dominant modes or basis functions from a collection of high-fidelity simulation snapshots, distinguished by its reliance on empirical data samples to approximate the solution manifold of a dynamical system.

61

Publications

3.8K

Citations

163

Authors

101

Institutions

Publications per year

2017–2026

20

Variants

Snapshot-based Model Order Reduction

Authors

163

  1. CW
    Clarence W. Rowley

    Princeton University, United States

    4 publications · 1.1K citations

    4

    h-index

  2. GR
    Gianluigi Rozza

    Scuola Internazionale Superiore di Studi Avanzati, Italy

    2 publications · 41 citations

    2

    h-index

  3. JL
    Jens Lang

    Technische Universität Darmstadt, Germany

    2 publications · 82 citations

    2

    h-index

  4. FB
    Francesco Ballarin

    Scuola Internazionale Superiore di Studi Avanzati, Italy

    2 publications · 41 citations

    2

    h-index

  5. SU
    Sebastian Ullmann

    Technische Universität Darmstadt, Germany

    2 publications · 82 citations

    2

    h-index

1–5 of 163

Venues

  1. 7

    h-index

  2. SIAM Journal on Scientific Computing

    4 publications · 161 citations

    4

    h-index

  3. AIAA Journal

    3 publications · 186 citations

    3

    h-index

  4. Journal of Computational Physics

    3 publications · 130 citations

    3

    h-index

  5. Physics of Fluids

    2 publications · 118 citations

    2

    h-index

1–5