5 papers
AI Agents in Engineering Design: A Multi-Agent Framework for Aesthetic and Aerodynamic Car Design
Mohamed Elrefaie, Janet Qian, Raina Wu +3
We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily…
TripNet: Learning Large-scale High-fidelity 3D Car Aerodynamics with Triplane Networks
Qian Chen, Mohamed Elrefaie, Angela Dai +1
Surrogate modeling has emerged as a powerful tool to accelerate Computational Fluid Dynamics (CFD) simulations. Existing 3D geometric learning models based on point clouds, voxels,…
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Mohamed Elrefaie, Florin Morar, Angela Dai +1
We present DrivAerNet++, the largest and most comprehensive multimodal dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fideli…
DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction
Mohamed Elrefaie, Angela Dai, Faez Ahmed
This study introduces DrivAerNet, a large-scale high-fidelity CFD dataset of 3D industry-standard car shapes, and RegDGCNN, a dynamic graph convolutional neural network model, both…
Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning
Mohamed Elrefaie, Steffen Hüttig, Mariia Gladkova +3
We introduce Recurrent All-Pairs Field Transforms for Stereoscopic Particle Image Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flow learning to analyze time-re…