Random Field Approximation

Random Field Approximation is a mathematical framework that belongs to the class of random field modeling techniques, distinguished by its purpose of constructing simplified stochastic representations to approximate complex spatially or temporally correlated random phenomena while preserving essential statistical and structural properties.

364

Publications

21.3K

Citations

710

Authors

411

Institutions

Publications per year

2017–2026

82

Authors

710

  1. MO
    Martin Ostoja‐Starzewski

    University of Illinois Urbana-Champaign, United States

    10 publications · 570 citations

    10

    h-index

  2. EP
    Emilio Porcu

    Federico Santa María Technical University, Chile

    7 publications · 142 citations

    7

    h-index

  3. PD
    Pol D. Spanos

    Rice University, United States

    6 publications · 187 citations

    6

    h-index

  4. HG
    Hermann G. Matthies

    Technische Universität Braunschweig, Germany

    6 publications · 231 citations

    6

    h-index

  5. AL
    Alexander Litvinenko

    Technische Universität Braunschweig, Germany

    5 publications · 192 citations

    5

    h-index

1–5 of 710

Venues

  1. Physical review. B, Condensed matter

    PRB · 14 publications · 1.8K citations

    12

    h-index

  2. 12

    h-index

  3. 9

    h-index

  4. Stochastic Processes and their Applications

    8 publications · 227 citations

    8

    h-index

  5. Physical Review Letters

    PRL · 8 publications · 577 citations

    8

    h-index

1–5