Adaptive Sampling Strategies

Adaptive Sampling Strategies is a sequential data acquisition methodology within multifidelity uncertainty quantification that iteratively selects the most informative sample locations and fidelity levels by balancing exploration and exploitation to improve model accuracy and efficiency.

756

Publications

44.1K

Citations

2.3K

Authors

859

Institutions

Publications per year

2017–2026

306

Authors

2.3K

  1. MZ
    Matthias Zwicker

    University of Maryland, College Park, United States

    7 publications · 598 citations

    7

    h-index

  2. SK
    Steven K. Thompson

    Pennsylvania State University, United States

    7 publications · 897 citations

    7

    h-index

  3. TD
    Tom Dhaene

    Ghent University, Belgium

    6 publications · 305 citations

    6

    h-index

  4. GS
    Gaurav S. Sukhatme

    University of Southern California, United States

    6 publications · 241 citations

    6

    h-index

  5. DA
    Derek A. Paley

    University of Maryland, College Park, United States

    5 publications · 972 citations

    5

    h-index

1–5 of 2.3K

Venues

  1. ACM Transactions on Graphics

    TOG · 18 publications · 1.4K citations

    17

    h-index

  2. Computer Graphics Forum

    17 publications · 728 citations

    15

    h-index

  3. arXiv (Cornell University)

    Repository · 25 publications · 533 citations

    15

    h-index

  4. Structural and Multidisciplinary Optimization

    9 publications · 760 citations

    9

    h-index

  5. Sensors

    8 publications · 214 citations

    7

    h-index

1–5