Data-driven Corrosion Prediction

Data-Driven Corrosion Prediction is a computational modeling approach that distinguishes itself by leveraging empirical data and statistical learning techniques to forecast corrosion behavior without relying primarily on mechanistic or theoretical assumptions.

132

Publications

5K

Citations

445

Authors

191

Institutions

Publications per year

2017–2026

115

Authors

445

  1. XL
    Xiaogang Li

    University of Science and Technology Beijing, China

    5 publications · 310 citations

    5

    h-index

  2. DZ
    Dawei Zhang

    University of Science and Technology Beijing, China

    4 publications · 314 citations

    4

    h-index

  3. FK
    Faisal Khan

    Memorial University of Newfoundland, Canada

    3 publications · 151 citations

    4

    h-index

  4. XL
    Xinhong Li

    Xi'an University of Architecture and Technology, China

    4 publications · 299 citations

    4

    h-index

  5. XC
    Xuequn Cheng

    University of Science and Technology Beijing, China

    4 publications · 300 citations

    4

    h-index

1–5 of 445

Venues

  1. Corrosion Science

    11 publications · 496 citations

    12

    h-index

  2. Process Safety and Environmental Protection

    8 publications · 348 citations

    9

    h-index

  3. Ocean Engineering

    3 publications · 139 citations

    5

    h-index

  4. Materials Today Communications

    5 publications · 126 citations

    5

    h-index

  5. CORROSION

    5 publications · 134 citations

    5

    h-index

1–5