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
Parents
16
Evaluation of POD based surrogate models of fields resulting from nonlinear FEM simulations
Boukje M. de Gooijer, Jos Havinga, H.J.M. Geijselaers et al. · Advanced Modeling and Simulation in Engineering Sciences · 2021 · 30 citations · Full text
Computing, Computational Engineering, Mechanical Engineering +14
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30

University of Dayton
Dayton, United States
2 h-index
4 publications
92 citations
Gdańsk University of Technology
Gdansk, Poland
1 h-index
1 publications
10 citations

Chinese Academy of Sciences
Beijing, China
1 h-index
1 publications
25 citations
Academy of Mathematics and Systems Science
Beijing, China
1 h-index
1 publications
25 citations

Université Toulouse III - Paul Sabatier
Toulouse, France
1 h-index
3 publications
45 citations
Universitat de Barcelona
Barcelona, Spain
1 h-index
3 publications
177 citations

Harvard University Press
Cambridge, United States
1 h-index
1 publications
27 citations
Université de Montréal
Montreal, Canada
1 h-index
1 publications
63 citations
1–8 of 30
2 publications · 48 citations
2
h-index
1 publications · 25 citations
1
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1 publications · 13 citations
1
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1 publications · 30 citations
1
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1 publications · 12 citations
1
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1–5