<ul><li>A new study proposes the DrivAer Transformer (DAT), a point cloud learning framework for evaluating vehicle aerodynamic performance.</li><li>The DAT structure uses the DrivAerNet++ dataset, containing high-fidelity CFD data of 3D vehicle shapes.</li><li>DAT enables accurate estimation of air drag directly from 3D meshes, avoiding limitations of traditional methods.</li><li>The framework is expected to accelerate the vehicle design process and improve development efficiency.</li></ul>

DrivAer Transformer: A high-precision and fast prediction method for vehicle aerodynamic drag coefficient based on the DrivAerNet++ dataset

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