{"ID":2828511,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.14397","arxiv_id":"2512.14397","title":"SuperWing: a comprehensive transonic wing dataset for data-driven aerodynamic design","abstract":"Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited by the scarcity and restricted diversity of existing datasets. Here, we present SuperWing, a comprehensive open dataset of transonic swept-wing aerodynamics comprising 4,239 parameterized wing geometries and 28,856 Reynolds-averaged Navier-Stokes flow field solutions. The wing shapes in the dataset are generated using a simplified yet expressive geometry parameterization that incorporates spanwise variations in airfoil shape, twist, and dihedral, allowing for an enhanced diversity without relying on perturbations of a baseline wing. All shapes are simulated under a broad range of Mach numbers and angles of attack covering the typical flight envelope. To demonstrate the dataset's utility, we benchmark two state-of-the-art Transformers that accurately predict surface flow and achieve a 2.5 drag-count error on held-out samples. Models pretrained on SuperWing further exhibit strong zero-shot generalization to complex benchmark wings such as DLR-F6 and NASA CRM, underscoring the dataset's diversity and potential for practical usage.","short_abstract":"Machine-learning surrogate models have shown promise in accelerating aerodynamic design, yet progress toward generalizable predictors for three-dimensional wings has been limited by the scarcity and restricted diversity of existing datasets. Here, we present SuperWing, a comprehensive open dataset of transonic swept-wi...","url_abs":"https://arxiv.org/abs/2512.14397","url_pdf":"https://arxiv.org/pdf/2512.14397v2","authors":"[\"Yunjia Yang\",\"Weishao Tang\",\"Mengxin Liu\",\"Nils Thuerey\",\"Yufei Zhang\",\"Haixin Chen\"]","published":"2025-12-16T13:35:45Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"physics.flu-dyn\"]","methods":"[\"Transformer\"]","has_code":false}