Deterministic Inverse Problems

Deterministic Inverse Problems is an inverse problem framework in which the underlying system is modeled without probabilistic assumptions, relying solely on exact mathematical relationships to uniquely infer unknown parameters from observed data.

67

Publications

3.7K

Citations

186

Authors

117

Institutions

Publications per year

2017–2026

18

Authors

186

  1. TB
    Troy Butler

    University of Colorado Denver, United States

    4 publications · 139 citations

    4

    h-index

  2. PK
    Prabhat K. Koner

    NOAA Center for Satellite Applications and Research, United States

    2 publications · 48 citations

    2

    h-index

  3. AN
    Arye Nehorai

    Washington University in St. Louis, United States

    2 publications · 69 citations

    2

    h-index

  4. AR
    Andrew R. Harris

    University College London, United Kingdom

    2 publications · 48 citations

    2

    h-index

  5. NV
    Nicolás von Ellenrieder

    Montreal Neurological Institute and Hospital, Canada

    2 publications · 69 citations

    2

    h-index

1–5 of 186

Venues

  1. Inverse Problems

    10 publications · 462 citations

    10

    h-index

  2. SIAM Journal on Numerical Analysis

    4 publications · 192 citations

    4

    h-index

  3. IEEE Transactions on Biomedical Engineering

    2 publications · 69 citations

    2

    h-index

  4. IEEE Transactions on Magnetics

    2 publications · 59 citations

    2

    h-index

  5. 2

    h-index

1–5