Stochastic Inverse Problems

Stochastic Inverse Problems is an inverse problem framework in which the unknown parameters or inputs are characterized as random variables or stochastic processes, and the solution involves inferring their probabilistic descriptions from noisy or uncertain observations.

318

Publications

23.7K

Citations

706

Authors

370

Institutions

Publications per year

2017–2026

90

Authors

706

  1. TB
    Troy Butler

    University of Colorado Denver, United States

    7 publications · 196 citations

    7

    h-index

  2. XX
    Xue‐Jun Xie

    Qufu Normal University, China

    6 publications · 390 citations

    6

    h-index

  3. AG
    Alberto Guadagnini

    Politecnico di Milano, Italy

    5 publications · 152 citations

    5

    h-index

  4. TW
    Timothy Wildey

    Sandia National Laboratories, United States

    5 publications · 152 citations

    5

    h-index

  5. DE
    Donald Estep

    Colorado State University, United States

    4 publications · 107 citations

    4

    h-index

1–5 of 706

Venues

  1. Inverse Problems

    20 publications · 998 citations

    17

    h-index

  2. Water Resources Research

    12 publications · 951 citations

    12

    h-index

  3. Linear Algebra and its Applications

    9 publications · 240 citations

    9

    h-index

  4. SIAM Journal on Scientific Computing

    8 publications · 355 citations

    8

    h-index

  5. Journal of Computational Physics

    7 publications · 1.2K citations

    7

    h-index

1–5