Data-driven Resistance Estimation

Data-Driven Resistance Estimation is a computational methodology within resistance prediction that employs empirical data and statistical learning techniques to model and predict resistance phenomena without relying primarily on first-principles physics-based assumptions.

22

Publications

2.5K

Citations

83

Authors

41

Institutions

Publications per year

2017–2026

17

Authors

83

  1. SG
    Sihai Guo

    Wuhan University of Technology, China

    1 publications · 10 citations

    1

    h-index

  2. XL
    Xueyou Li

    Kunming Institute of Zoology, China

    1 publications · 35 citations

    1

    h-index

  3. SL
    Shawn Litster

    Carnegie Mellon University, United States

    1 publications · 39 citations

    1

    h-index

  4. İK
    İbrahim Karaman

    Texas A&M University, United States

    1 publications · 15 citations

    1

    h-index

  5. JX
    Jun Xu

    Beihang University, China

    1 publications · 30 citations

    1

    h-index

1–5 of 83

Venues

  1. Engineering Structures

    2 publications · 60 citations

    2

    h-index

  2. 1

    h-index

  3. Composite Structures

    1 publications · 20 citations

    1

    h-index

  4. Journal of Hydroinformatics

    1 publications · 344 citations

    1

    h-index

  5. Journal of Energy Chemistry

    1 publications · 30 citations

    1

    h-index

1–5