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 (out of 104 accepted papers) 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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