Model-based Parameter Estimation

Model-Based Parameter Estimation is a methodological framework that employs a predefined mathematical or physical model to infer unknown system parameters from observed data by optimizing the fit between the model's predicted outputs and the measured sparse samples, while explicitly accounting for artifacts such as wellbore storage effects.

655

Publications

35.2K

Citations

2K

Authors

807

Institutions

Publications per year

2017–2026

180

Authors

2K

  1. EM
    E.K. Miller

    Lawrence Livermore National Laboratory, United States

    5 publications · 471 citations

    5

    h-index

  2. DH
    Douglas H. Werner

    Pennsylvania State University, United States

    5 publications · 171 citations

    5

    h-index

  3. PS
    Paul Stapor

    Helmholtz Zentrum München, Germany

    4 publications · 285 citations

    4

    h-index

  4. JD
    Jay D. Martin

    Pennsylvania State University, United States

    4 publications · 1.1K citations

    4

    h-index

  5. FF
    Fabian Fröhlich

    Technical University of Munich, Germany

    4 publications · 285 citations

    4

    h-index

1–5 of 2K

Venues

  1. Computers & Chemical Engineering

    9 publications · 307 citations

    9

    h-index

  2. Water Resources Research

    7 publications · 491 citations

    7

    h-index

  3. IEEE Transactions on Antennas and Propagation

    7 publications · 384 citations

    7

    h-index

  4. IEEE Transactions on Signal Processing

    6 publications · 259 citations

    6

    h-index

  5. PLoS Computational Biology

    6 publications · 1K citations

    6

    h-index

1–5