What is recorded here?
Evolved 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.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
One 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.
Read the original work
- NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics Simulations
- AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
- AB-UPT for Automotive and Aerospace Applications
- Mistral research publications index
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Maurits Bleeker; Matthias Dorfer; Tobias Kronlachner; Reinhard Sonnleitner; Benedikt Alkin; Johannes Brandstetter; Richard Kurle; Louis Serrano; Dennis Just
Source-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.
Research locations and affiliations
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English catalogue narrative
NeuralCFD / AB-UPT: geometry-separated anchored CFD surrogates
Evolved 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.
Scientific scope
One 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.
Reading status
This 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.
People and systems
Maurits Bleeker; Matthias Dorfer; Tobias Kronlachner; Reinhard Sonnleitner; Benedikt Alkin; Johannes Brandstetter; Richard Kurle; Louis Serrano; Dennis Just
Source-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.
Original sources
- NeuralCFD: Deep Learning on High-Fidelity Automotive Aerodynamics Simulations
- AB-UPT: Scaling Neural CFD Surrogates for High-Fidelity Automotive Aerodynamics Simulations via Anchored-Branched Universal Physics Transformers
- AB-UPT for Automotive and Aerospace Applications
- Mistral research publications index