Sparse Surrogate Models

Sparse Surrogate Models is a class of surrogate models that belong to the broader category of approximation models used in computational science and engineering, distinguished by their reliance on sparse representations—employing a minimal set of basis functions, samples, or features—to achieve computational efficiency and interpretability while maintaining predictive accuracy.

127

Publications

5.8K

Citations

397

Authors

213

Institutions

Publications per year

2017–2026

70

Variants

Sparse Surrogate Model

Authors

397

  1. BS
    Bruno Sudret

    ETH Zurich, Switzerland

    4 publications · 121 citations

    4

    h-index

  2. DS
    Dinggang Shen

    University of North Carolina at Chapel Hill, United States

    3 publications · 418 citations

    3

    h-index

  3. NB
    Nathan Baker

    Washington University in St. Louis, United States

    2 publications · 47 citations

    2

    h-index

  4. MB
    Massimo Bonavita

    European Centre for Medium-Range Weather Forecasts, United Kingdom

    2 publications · 161 citations

    2

    h-index

  5. SM
    Stefano Marelli

    ETH Zurich, Switzerland

    2 publications · 61 citations

    2

    h-index

1–5 of 397

Venues

  1. 5

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  2. Journal of Computational Physics

    3 publications · 493 citations

    3

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 127 citations

    3

    h-index

  4. Computers & Chemical Engineering

    3 publications · 134 citations

    3

    h-index

  5. 2

    h-index

1–5