{"ID":2836457,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21474","arxiv_id":"2511.21474","title":"Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes","abstract":"The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity of compressible flows and 3D effects such as wingtip vortices. Existing aerospace datasets predominantly focus on 2D airfoils, neglecting these critical 3D phenomena. To address this gap, we present a new dataset of CFD simulations for 3D wings in the transonic regime. The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions. This allows computation of lift and drag coefficients, providing a foundation for data-driven aerodynamic optimization of the drag-lift Pareto front. We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT, focusing on their out-of-distribution (OOD) generalization over geometry and inflow variations. AB-UPT demonstrates strong performance for transonic flowfields and reproduces physically consistent drag-lift Pareto fronts even for unseen wing configurations. Our results demonstrate that AB-UPT can approximate drag-lift Pareto fronts for unseen geometries, highlighting its potential as an efficient and effective tool for rapid aerodynamic design exploration. To facilitate future research, we open-source our dataset at https://huggingface.co/datasets/EmmiAI/Emmi-Wing.","short_abstract":"The widespread use of neural surrogates in automotive aerodynamics, enabled by datasets such as DrivAerML and DrivAerNet++, has primarily focused on bluff-body flows with large wakes. Extending these methods to aerospace, particularly in the transonic regime, remains challenging due to the high level of non-linearity o...","url_abs":"https://arxiv.org/abs/2511.21474","url_pdf":"https://arxiv.org/pdf/2511.21474v2","authors":"[\"Fabian Paischer\",\"Leo Cotteleer\",\"Yann Dreze\",\"Richard Kurle\",\"Dylan Rubini\",\"Maurits Bleeker\",\"Tobias Kronlachner\",\"Johannes Brandstetter\"]","published":"2025-11-26T15:06:19Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\",\"cs.LG\"]","methods":"[\"LoRA\",\"Variational Autoencoder\"]","has_code":false}