Data-driven Ventilation Modeling

Data-Driven Ventilation Modeling is a computational modeling approach within ventilation science that distinguishes itself by leveraging empirical data and statistical learning techniques to infer, optimize, or simulate airflow and contaminant dispersion dynamics without relying exclusively on first-principles physical equations.

8

Publications

404

Citations

48

Authors

24

Institutions

Publications per year

2017–2026

6

Authors

48

  1. JL
    Jiqin Li

    Harbin Institute of Technology, China

    1 publications · 15 citations

    1

    h-index

  2. DB
    Daniël Berckmans

    KU Leuven, Belgium

    1 publications · 22 citations

    1

    h-index

  3. HC
    Hongtao Cheng

    Jinan University, China

    1 publications · 43 citations

    1

    h-index

  4. EV
    Erik Vranken

    KU Leuven, Belgium

    1 publications · 22 citations

    1

    h-index

  5. XC
    Xinyi Chen

    Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, China

    1 publications · 12 citations

    1

    h-index

1–5 of 48

Venues

  1. Building and Environment

    4 publications · 187 citations

    4

    h-index

  2. medRxiv

    Repository · 1 publications · 12 citations

    1

    h-index

  3. Heart & Lung

    1 publications · 43 citations

    1

    h-index

  4. Building Simulation

    1 publications · 46 citations

    1

    h-index

  5. Artificial Intelligence in Medicine

    AIM · 1 publications · 116 citations

    1

    h-index

1–5