Model Reduction

Model Reduction is a methodological framework within mathematical and computational modeling that systematically simplifies high-dimensional or complex dynamical systems—such as those arising in Computational Solid Mechanics, State-Space Representation, or Systems Pharmacology Modeling—by retaining essential input-output, structural, or dynamical features while eliminating or aggregating less influential states or parameters, thereby enabling efficient simulation, analysis, and control design without significant loss of accuracy.

3.7K

Publications

191K

Citations

6.7K

Authors

1.8K

Institutions

Publications per year

2017–2026

1K

Authors

6.7K

  1. KW
    Karen Willcox

    Massachusetts Institute of Technology, United States

    43 publications · 7.2K citations

    30

    h-index

  2. PB
    Peter Benner

    Max Planck Institute for Dynamics of Complex Technical Systems, Germany

    79 publications · 5.1K citations

    29

    h-index

  3. SG
    Serkan Gugercin

    Virginia Tech, United States

    32 publications · 4.9K citations

    23

    h-index

  4. CF
    Charbel Farhat

    Stanford University, United States

    19 publications · 3K citations

    19

    h-index

  5. VS
    Victor Sreeram

    The University of Western Australia, Australia

    32 publications · 1.2K citations

    19

    h-index

1–5 of 6.7K

Venues

  1. IEEE Transactions on Automatic Control

    140 publications · 14.7K citations

    48

    h-index

  2. International Journal of Control

    89 publications · 6K citations

    40

    h-index

  3. 38

    h-index

  4. 37

    h-index

  5. SIAM Journal on Scientific Computing

    62 publications · 5.9K citations

    33

    h-index

1–5