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
Parents
8
1–5 of 8
48
1
h-index
1
h-index
1
h-index
1
h-index
Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, China
1 publications · 12 citations
1
h-index
1–5 of 48
24
National University of Singapore
Singapore, Singapore
2 h-index
2 publications
153 citations
Hogeschool de Kempel
Helmond, The Netherlands
1 h-index
1 publications
22 citations

Lawrence Berkeley National Laboratory
Berkeley, United States
1 h-index
1 publications
107 citations
First Affiliated Hospital of Jinan University
Guangzhou, China
1 h-index
6 publications
258 citations
Princeton University
Princeton, United States
1 h-index
12 publications
144 citations

Jinan University
Guangzhou, China
1 h-index
2 publications
86 citations

Xi'an Jiaotong University
Xi'an, China
1 h-index
1 publications
43 citations
Neuroscience Institute
Pisa, Italy
1 h-index
1 publications
12 citations
1–8 of 24
4 publications · 187 citations
4
h-index
Repository · 1 publications · 12 citations
1
h-index
1 publications · 43 citations
1
h-index
1 publications · 46 citations
1
h-index
AIM · 1 publications · 116 citations
1
h-index
1–5