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README.md

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@@ -78,6 +78,16 @@ DrivAerNet++ supports a wide array of machine learning applications, including b
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- 🌐 **Part and shape classification**: Classify car categories or components to enhance design analysis.
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- 🔧 **Automated CFD meshing**: Automate the meshing process based on car components to streamline simulations.
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## Dataset Access & Download
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The DrivAerNet++ dataset is hosted under the **CC BY-NC 4.0** license on [Harvard Dataverse](https://dataverse.harvard.edu/dataverse/DrivAerNet). The dataset is structured into four subsets:
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- **3D Meshes**: Parametric car geometries in STL format.
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- **Pressure**: Surface pressure field data.
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- **Wall Shear Stress**: Aerodynamic wall shear stress distributions.
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- **CFD (Full CFD Domain)**: Complete volumetric CFD simulation data.
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We provide instructions on how to use Globus and to download the dataset. Please feel free to reach out if you encounter any problems/issues.
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## Results Replication
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To cite the first version of DrivAerNet, please use the following reference:
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```bibtex
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@proceedings{elrefaie2024drivaernet,
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author = {Elrefaie, Mohamed and Ahmed, Faez and Dai, Angela},
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@proceedings{10.1115/DETC2024-143593,
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author = {Elrefaie, Mohamed and Dai, Angela and Ahmed, Faez},
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title = {DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction},
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volume = {Volume 3A: 50th Design Automation Conference (DAC)},
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series = {International Design Engineering Technical Conferences and Computers and Information in Engineering Conference},
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month = {08},
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doi = {10.1115/DETC2024-143593},
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url = {https://doi.org/10.1115/DETC2024-143593},
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eprint = {https://asmedigitalcollection.asme.org/IDETC-CIE/proceedings-pdf/IDETC-CIE2024/88360/V03AT03A019/7402927/v03at03a019-detc2024-143593.pdf},
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}
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