{"url":"/dataset/drivaernet","name":"DrivAerNet","full_name":"A Parametric Car Dataset for Data-driven Aerodynamic Design and Graph-Based Drag Prediction","description_markdown":"DrivAerNet is a large-scale, high-fidelity CFD dataset of 3D industry-standard car shapes designed for data-driven aerodynamic design. It comprises 4000 high-quality 3D car meshes and their corresponding aerodynamic performance coefficients, alongside full 3D flow field information.\r\n\r\n It includes:\r\n\r\n- **CFD Simulation Data**: The raw dataset, including full 3D pressure, velocity fields, and wall-shear stresses, computed using **8-16 million mesh elements** has a total size of $\\sim$ **16TB**.\r\n- **Curated CFD Simulations**: For ease of access and use, a **streamlined version of the CFD simulation data** is provided, refined to include key insights and data, reducing the size to $\\sim$ **1TB**. \r\n- **3D Car Meshes**: A total of **4000 designs**, showcasing a variety of conventional car shapes and emphasizing the impact of minor geometric modifications on aerodynamic efficiency. The 3D meshes and aerodynamic coefficients $\\sim$ **84GB**.\r\n- 2D slices include the car's wake in the $x$-direction and the symmetry plane in the $y$-direction $\\sim$ **12GB**.","description_withheld":null,"homepage":"https://github.com/Mohamedelrefaie/DrivAerNet","introduced_date":"2024-03-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/drivaernet-a-parametric-car-dataset-for-data","title":"DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction","first_author":"Mohamed Elrefaie","url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"Tabular","url":"/datasets/modality/tabular"},{"name":"3d meshes","url":"/datasets/modality/3d-meshes"},{"name":"Physics","url":"/datasets/modality/physics"}],"tasks":[{"name":"Graph Regression","url":"/task/graph-regression","datasets_with_task":"/datasets/task/graph-regression"},{"name":"Physical Simulations","url":"/task/physical-simulations","datasets_with_task":"/datasets/task/physical-simulations"},{"name":"3D Shape Modeling","url":"/task/3d-shape-modeling","datasets_with_task":"/datasets/task/3d-shape-modeling"},{"name":"3D Geometry Prediction","url":"/task/3d-geometry-prediction","datasets_with_task":"/datasets/task/3d-geometry-prediction"},{"name":"3D Anomaly Detection and Segmentation","url":"/task/3d-anomaly-detection-and-segmentation","datasets_with_task":"/datasets/task/3d-anomaly-detection-and-segmentation"},{"name":"Parameter Prediction","url":"/task/parameter-prediction","datasets_with_task":"/datasets/task/parameter-prediction"},{"name":"Physics-informed machine learning","url":"/task/physics-informed-machine-learning","datasets_with_task":"/datasets/task/physics-informed-machine-learning"},{"name":"PDE Surrogate Modeling","url":"/task/pde-surrogate-modeling","datasets_with_task":"/datasets/task/pde-surrogate-modeling"}],"languages":[],"variants":["DrivAerNet"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}