25 citations · 31 across the 6 of their papers we have counts for
6 papers
A Hierarchical Deep Learning Model for Predicting Pedestrian-Level Urban Winds
Reda Snaiki, Jiachen Lu, Shaopeng Li +1
Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. H…
Advancing Spatio-temporal Storm Surge Prediction with Hierarchical Deep Neural Networks
Saeed Saviz Naeini, Reda Snaiki, Teng Wu
Coastal regions in North America face major threats from storm surges caused by hurricanes and nor'easters. Traditional numerical models, while accurate, are computationally expens…
Real-Time Dynamic Layout Optimization for Floating Offshore Wind Farm Control
Timothé Jard, Reda Snaiki
Downstream wind turbines operating behind upstream turbines face significant performance challenges due to reduced wind speeds and increased turbulence. This leads to decreased win…
A Physics-informed machine learning model for time-dependent wave runup prediction
Saeed Saviz Naeini, Reda Snaiki
Wave runup is a critical factor affecting coastal flooding, shoreline changes, and damage to coastal structures. Climate change is also expected to amplify wave runup's impact on c…
Performance-based ice engineering: a data-driven multi-scale approach
Reda Snaiki
Ice storms are one of the most devastating natural hazards which have the potential to inflict significant damage to the built environment. The multi-hazard nature of ice events co…
A Novel Hybrid Machine Learning Model for Rapid Assessment of Wave and Storm Surge Responses Over an Extended Coastal Region
Saeed Saviz Naeini, Reda Snaiki
Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly adva…