Adaptive Sampling

Adaptive Sampling is a spatial sampling strategy that iteratively selects sample locations based on previously observed data, distinguishing itself from non-adaptive methods by dynamically adjusting the sampling design to optimize information gain or estimation accuracy.

1.3K

Publications

73K

Citations

3.6K

Authors

1.2K

Institutions

Publications per year

2017–2026

488

Authors

3.6K

  1. SK
    Steven K. Thompson

    Pennsylvania State University, United States

    14 publications · 1.3K citations

    12

    h-index

  2. GS
    Gaurav S. Sukhatme

    University of Southern California, United States

    9 publications · 307 citations

    9

    h-index

  3. MZ
    Matthias Zwicker

    University of Maryland, College Park, United States

    9 publications · 753 citations

    9

    h-index

  4. DR
    David R. Smith

    United States Geological Survey, United States

    8 publications · 323 citations

    8

    h-index

  5. TD
    Tom Dhaene

    Ghent University, Belgium

    7 publications · 753 citations

    7

    h-index

1–5 of 3.6K

Venues

  1. ACM Transactions on Graphics

    TOG · 25 publications · 2.2K citations

    21

    h-index

  2. arXiv (Cornell University)

    Repository · 40 publications · 976 citations

    18

    h-index

  3. Computer Graphics Forum

    23 publications · 945 citations

    17

    h-index

  4. IEEE Access

    15 publications · 480 citations

    13

    h-index

  5. Journal of Chemical Theory and Computation

    JCTC · 12 publications · 1.5K citations

    12

    h-index

1–5