Mohamed Elrefaie

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. Currently, I am working on developing foundation physics models—AI models that can understand the physical world and apply this understanding to engineering design and simulations.

Highlights

Dec 5, 2024

Thrilled to see my recent work on DrivAerNet++ featured as a spotlight by MIT News! Proud to contribute to open science and innovation in engineering design.

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%, with over 16,000 submissions this year)

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 (MIT Research Spotlight)

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 (Springer Nature), 2024

Mohamed Elrefaie

About

Highlights

Selected Publications

Contact