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      "statement": "# NeuralCFD / AB-UPT: geometry-separated anchored CFD surrogates\n\nEvolved GP-UPT geometry-preserving encoding into anchored, multi-branch UPT decoders for surface/volume fields, including an explicit divergence-free output formulation; a later report evaluated existing architecture on SHIFT automotive/aerospace data.\n\n## Scientific scope\n\nOne original-preprint/revised-method/application family. First v1 is GP-UPT with6authors; v4 AB-UPT has7and changed architecture, not backdated as the February formulation. Additional application report states no major architecture change. Surrogate errors/speedups are finite numerical comparisons, not exact NavierStokes solutions or universal physical constraints.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nMaurits Bleeker; Matthias Dorfer; Tobias Kronlachner; Reinhard Sonnleitner; Benedikt Alkin; Johannes Brandstetter; Richard Kurle; Louis Serrano; Dennis Just\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics Simulations](https://arxiv.org/abs/2502.09692v1)\n- [AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers](https://arxiv.org/abs/2502.09692v4)\n- [AB-UPT for Automotive and Aerospace Applications](https://arxiv.org/abs/2510.15808v1)\n- [Mistral research publications index](https://mistral.ai/research/)\n",
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