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

Authors

1.3K

  1. RB
    Robert B. Gramacy

    University of Chicago, United States

    18 publications · 1.4K citations

    16

    h-index

  2. YO
    Yew-Soon Ong

    Nanyang Technological University, Singapore

    6 publications · 677 citations

    6

    h-index

  3. HK
    Herbert K. H. Lee

    University of California, Santa Cruz, United States

    5 publications · 465 citations

    5

    h-index

  4. AG
    Andrew Gordon Wilson

    Cornell University, United States

    5 publications · 191 citations

    5

    h-index

  5. TC
    Tao Chen

    University of Surrey, United Kingdom

    5 publications · 333 citations

    5

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 28 publications · 892 citations

    18

    h-index

  2. Reliability Engineering & System Safety

    15 publications · 1.1K citations

    15

    h-index

  3. Technometrics

    15 publications · 1.5K citations

    13

    h-index

  4. Mechanical Systems and Signal Processing

    MSSP · 10 publications · 324 citations

    10

    h-index

  5. 9

    h-index

1–5