Mohamed Elrefaie

Building foundation physics models

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About

I am a recent graduate of the Technical University of Munich (TUM) with a bachelor’s in Mechanical Engineering and a master’s in Aerospace. I spent a year as a graduate research assistant at MIT's DeCoDE Lab. My research integrates deep learning with computational and experimental fluid dynamics to advance aerodynamic design. I am currently working on building foundation physics models for engineering design and simulations.

Highlights

Oct 20, 2024

DrivAerNet++ was awarded the MIT Prize for Open Data in recognition of its contribution to advancing accessible research

Sep 1, 2024

DrivAerNet++ has been accepted to NeurIPS 2024, the top-tier AI conference (Acceptance rate: 25.3%)

Aug 20, 2024

DrivAerNet paper received the Paper of Distinction Award from The American Society of Mechanical Engineers at IDETC in Washington, D.C.

Selected Publications

DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks

Mohamed Elrefaie, Florin Morar, Angela Dai, Faez Ahmed

NeurIPS 2024

Real-time and on-site aerodynamics using stereoscopic piv and deep optical flow learning

Mohamed Elrefaie, Steffen Hüttig, Mariia Gladkova, Timo Gericke, Daniel Cremers, Christian Breitsamter

Experiments in Fluids, 2024

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