An aerodynamic dataset for AI-driven wind-resistant design optimization of bridges

Presentation initial slide.

Abstract

This presentation showcases a method for aerodynamic shape optimization of bridge decks. It involves generating a comprehensive aerodynamic database using CFD simulations and then training surrogate models on the data to expedite efficient evaluation of aerodynamic performance of different shapes.

Publication
SimCenter Community Roundtable: Fostering computational wind simulations in research and practice, Working Group on Wind and Water Simulation
Omar A. Mures
Omar A. Mures
Instructor

My research interests include Deep Learning, Computer Vision and Computer Graphics.