Interpolation Surrogates

Interpolation Surrogates is a surrogate modeling approach that belongs to the class of data-driven approximation methods, distinguished by its requirement to pass exactly through all available training data points, thereby ensuring zero prediction error at sample locations.

16

Publications

377

Citations

42

Authors

30

Institutions

Publications per year

2017–2026

9

Variants

Interpolating Surrogates

Authors

42

  1. MP
    Markus P. Rumpfkeil

    University of Dayton, United States

    2 publications · 46 citations

    2

    h-index

  2. KB
    Komahan Boopathy

    Georgia Institute of Technology, United States

    2 publications · 46 citations

    2

    h-index

  3. BM
    Boukje M. de Gooijer

    University of Twente, Netherlands

    1 publications · 30 citations

    1

    h-index

  4. JK
    Juho Kannala

    Aalto University, Finland

    1 publications · 27 citations

    1

    h-index

  5. MS
    Michel Salaün

    Université Fédérale de Toulouse Midi-Pyrénées, France

    1 publications · 15 citations

    1

    h-index

1–5 of 42

Venues

  1. AIAA Journal

    2 publications · 48 citations

    2

    h-index

  2. Technometrics

    1 publications · 25 citations

    1

    h-index

  3. Nature Machine Intelligence

    1 publications · 13 citations

    1

    h-index

  4. 1

    h-index

  5. Asian Journal of Control

    1 publications · 12 citations

    1

    h-index

1–5