Random Field Modeling

Random Field Modeling is a probabilistic modeling framework in uncertainty quantification that represents spatially or spatio-temporally varying uncertain quantities as random fields—stochastic processes indexed over a domain with prescribed marginal distributions and dependence structure—in order to infer, propagate, and analyze uncertainty in complex systems.

1.5K

Publications

75.5K

Citations

2.6K

Authors

1.1K

Institutions

Publications per year

2017–2026

298

Authors

2.6K

  1. CS
    Christian Soize

    Laboratoire Modélisation et Simulation Multi-Echelle, France

    16 publications · 1K citations

    15

    h-index

  2. XE
    Xavier Emery

    University of Chile, Chile

    19 publications · 686 citations

    14

    h-index

  3. RJ
    Robert J. Adler

    Technion – Israel Institute of Technology, Israel

    14 publications · 1.9K citations

    13

    h-index

  4. EP
    Emilio Porcu

    Federico Santa María Technical University, Chile

    16 publications · 476 citations

    13

    h-index

  5. CM
    Chunsheng Ma

    Wichita State University, United States

    18 publications · 350 citations

    13

    h-index

1–5 of 2.6K

Venues

  1. Water Resources Research

    39 publications · 3.5K citations

    27

    h-index

  2. 24

    h-index

  3. Journal of Engineering Mechanics

    32 publications · 3.2K citations

    23

    h-index

  4. 18

    h-index

  5. IEEE Transactions on Information Theory

    27 publications · 1.3K citations

    17

    h-index

1–5