Gaussian Process Surrogates
Gaussian Process Surrogates is a statistical modeling framework that employs Gaussian processes as a probabilistic surrogate to approximate computationally expensive functions by interpolating observed data with quantified uncertainty.
449
Publications
27.8K
Citations
1.3K
Authors
538
Institutions
Publications per year
2017–2026
305
Variants
Gaussian Process Surrogate Models
Parents
449
Improved Surrogate Data for Nonlinearity Tests
Thomas Schreiber, Andreas Schmitz · Physical Review Letters · 1996 · 1.5K citations · Full text
Computing, Biostatistics, Statistics +13
1–5 of 449
1.3K
16
h-index
6
h-index
5
h-index
5
h-index
5
h-index
1–5 of 1.3K
538
Virginia Tech
Blacksburg, United States
13 h-index
33 publications
1.2K citations

Centre National de la Recherche Scientifique
Paris, France
12 h-index
30 publications
1K citations

University of Exeter
Exeter, United Kingdom
10 h-index
19 publications
602 citations
Texas A&M University
College Station, United States
10 h-index
36 publications
2.1K citations
Georgia Institute of Technology
Atlanta, United States
9 h-index
25 publications
2.3K citations
Northwestern University
Evanston, United States
8 h-index
20 publications
930 citations
University of California, Santa Cruz
Santa Cruz, United States
8 h-index
10 publications
946 citations
University of Sheffield
Sheffield, United Kingdom
8 h-index
23 publications
2.4K citations
1–8 of 538
Repository · 28 publications · 892 citations
18
h-index
15 publications · 1.1K citations
15
h-index
15 publications · 1.5K citations
13
h-index
MSSP · 10 publications · 324 citations
10
h-index
10 publications · 358 citations
9
h-index
1–5