From large-eddy simulations to deep learning
A U-net model for fast urban canopy flow predictions
Bibliographic Data
| ID | 21233876 |
|---|---|
| Authors | Themistoklis Vargiemezis (0000-0001-5607-025X, Stanford University, corresponding author), Catherine Gorlé (0000-0001-8281-6545, Stanford University) |
| Year | 2025 |
| Volume | 135 |
| Pages | 107005 |
| Publication date | 2025-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2025.107005 |
| OpenAlex | W4416528721 |
| Language | EN |
| Citations received | 2 |
| References cited | 36 |
Atmospheric model · Computation · Computational fluid dynamics · Inflow · Turbulence · Wind direction · Wind speed · Meteorological Phenomena and Simulations · Urban Heat Island Mitigation · Wind and Air Flow Studies
An improved LOD specification for 3D building models
Computational Fluid Dynamics for urban physics
A CFD study of wind assessment in urban topology with complex wind flow
City configurations to optimise pedestrian level ventilation and wind comfort
Pedestrian level wind assessment through city development
Unlocking neighborhood density
| Citations per year | 2 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |