DrivAerNet++ is the largest multimodal dataset for aerodynamic car design, containing 8,150 diverse car geometries with associated high-fidelity computational fluid dynamics simulations. The repository provides benchmark baselines using deep neural networks (DGCNN, graph neural networks) for surrogate modeling of fluid dynamics. It includes train/test splits and evaluation protocols for neural surrogate tasks, published at NeurIPS 2024.