Data-driven Flow Regime Identification

Data-Driven Flow Regime Identification is a methodology in fluid dynamics that uses empirical data and machine learning techniques to classify flow regimes without relying on first-principles models or predefined physical assumptions.

16

Publications

737

Citations

45

Authors

23

Institutions

Publications per year

2017–2026

14

Authors

45

  1. UK
    Umair Khan

    Universiti Teknologi Petronas, Malaysia

    1 publications · 16 citations

    2

    h-index

  2. KE
    Karl Ezra Pilario

    University of the Philippines Diliman, Philippines

    1 publications · 16 citations

    2

    h-index

  3. WP
    William Pao

    Universiti Teknologi Petronas, Malaysia

    1 publications · 16 citations

    2

    h-index

  4. NS
    Nabihah Sallih

    Universiti Teknologi Petronas, Malaysia

    1 publications · 16 citations

    2

    h-index

  5. SZ
    Suqi Zhang

    Chinese Academy of Sciences, China

    1 publications · 26 citations

    1

    h-index

1–5 of 45

Venues

  1. Annual Review of Fluid Mechanics

    2 publications · 154 citations

    2

    h-index

  2. Engineering Analysis with Boundary Elements

    1 publications · 16 citations

    1

    h-index

  3. International Journal of Multiphase Flow

    1 publications · 33 citations

    1

    h-index

  4. Applied Radiation and Isotopes

    1 publications · 58 citations

    1

    h-index

  5. Measurement

    1 publications · 27 citations

    1

    h-index

1–5