Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes

• URL: http://arxiv.org/abs/2511.21474v1
• Date: Wed, 26 Nov 2025 15:06:19 GMT
• Title: Going with the Speed of Sound: Pushing Neural Surrogates into Highly-turbulent Transonic Regimes
• Authors: Fabian Paischer, Leo Cotteleer, Yann Dreze, Richard Kurle, Dylan Rubini, Maurits Bleeker, Tobias Kronlachner, Johannes Brandstetter,
• Abstract summary: We present a new dataset of CFD simulations for 3D wings in the transonic regime.<n>The dataset comprises volumetric and surface-level fields for around $30,000$ samples with unique geometry and inflow conditions.<n>We evaluate several state-of-the-art neural surrogates on our dataset, including Transolver and AB-UPT.
• Score: 19.286954413935025
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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