  We present DrivAerNet++, the largest and most comprehensive multimodal
dataset for aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car
designs modeled with high-fidelity computational fluid dynamics (CFD)
simulations. The dataset includes diverse car configurations such as fastback,
notchback, and estateback, with different underbody and wheel designs to
represent both internal combustion engines and electric vehicles. Each entry in
the dataset features detailed 3D meshes, parametric models, aerodynamic
coefficients, and extensive flow and surface field data, along with segmented
parts for car classification and point cloud data. This dataset supports a wide
array of machine learning applications including data-driven design
optimization, generative modeling, surrogate model training, CFD simulation
acceleration, and geometric classification. With more than 39 TB of publicly
available engineering data, DrivAerNet++ fills a significant gap in available
resources, providing high-quality, diverse data to enhance model training,
promote generalization, and accelerate automotive design processes. Along with
rigorous dataset validation, we also provide ML benchmarking results on the
task of aerodynamic drag prediction, showcasing the breadth of applications
supported by our dataset. This dataset is set to significantly impact
automotive design and broader engineering disciplines by fostering innovation
and improving the fidelity of aerodynamic evaluations.
