  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 aimed at aerodynamic car design through machine
learning. DrivAerNet, with its 4000 detailed 3D car meshes using 0.5 million
surface mesh faces and comprehensive aerodynamic performance data comprising of
full 3D pressure, velocity fields, and wall-shear stresses, addresses the
critical need for extensive datasets to train deep learning models in
engineering applications. It is 60\% larger than the previously available
largest public dataset of cars, and is the only open-source dataset that also
models wheels and underbody. RegDGCNN leverages this large-scale dataset to
provide high-precision drag estimates directly from 3D meshes, bypassing
traditional limitations such as the need for 2D image rendering or Signed
Distance Fields (SDF). By enabling fast drag estimation in seconds, RegDGCNN
facilitates rapid aerodynamic assessments, offering a substantial leap towards
integrating data-driven methods in automotive design. Together, DrivAerNet and
RegDGCNN promise to accelerate the car design process and contribute to the
development of more efficient vehicles. To lay the groundwork for future
innovations in the field, the dataset and code used in our study are publicly
accessible at \url{https://github.com/Mohamedelrefaie/DrivAerNet}
