Conditional Realization Generation

Conditional Realization Generation is a stochastic simulation method that produces spatially or temporally consistent realizations of a random field conditioned on observed data, distinguished by its incorporation of auxiliary information to honor both the statistical model and hard constraints.

15

Publications

470

Citations

55

Authors

16

Institutions

Publications per year

2017–2026

12

Authors

55

  1. FG
    Frank Guérin

    University of Aberdeen, United Kingdom

    2 publications · 26 citations

    2

    h-index

  2. CL
    Chenghua Lin

    University of Sheffield, United Kingdom

    2 publications · 26 citations

    2

    h-index

  3. XL
    Xiao Li

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 26 citations

    2

    h-index

  4. RL
    Ruizhe Li

    University of Aberdeen, United Kingdom

    2 publications · 26 citations

    2

    h-index

  5. CW
    Chaozheng Wang

    Harbin Institute of Technology, China

    2 publications · 26 citations

    2

    h-index

1–5 of 55

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 96 citations

    5

    h-index

  2. Water Resources Research

    2 publications · 65 citations

    2

    h-index

  3. Aberdeen University Research Archive (Aberdeen University)

    Repository · 1 publications · 14 citations

    1

    h-index

  4. Journal of Hydrologic Engineering

    1 publications · 12 citations

    1

    h-index

  5. Laser & Photonics Review

    1 publications · 19 citations

    1

    h-index

1–5