Machine Learning-based Inversion

Machine Learning-Based Inversion is a computational methodology within inverse heat transfer that employs machine learning models to infer unknown thermal properties, boundary conditions, or initial conditions from measured temperature data by learning the underlying mapping between inputs and outputs through data-driven training.

284

Publications

13.5K

Citations

1K

Authors

373

Institutions

Publications per year

2017–2026

218

Authors

1K

  1. TA
    Tariq Alkhalifah

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 137 citations

    5

    h-index

  2. JH
    J.A. Hernández

    Universidad Autónoma del Estado de Morelos, Mexico

    5 publications · 169 citations

    5

    h-index

  3. BW
    Bangyu Wu

    Xi'an Jiaotong University, China

    4 publications · 231 citations

    4

    h-index

  4. DM
    Delin Meng

    Xi'an Jiaotong University, China

    4 publications · 231 citations

    4

    h-index

  5. ML
    Maokun Li

    Tsinghua University, China

    4 publications · 260 citations

    4

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 496 citations

    12

    h-index

  2. Geophysics

    10 publications · 455 citations

    10

    h-index

  3. IEEE Transactions on Antennas and Propagation

    8 publications · 577 citations

    8

    h-index

  4. 6

    h-index

  5. IEEE Geoscience and Remote Sensing Letters

    GRSL · 5 publications · 264 citations

    5

    h-index

1–5