| Field                                                                                                  | Response                                                                                                                                                                                                  |
|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Application & Domain:                                                                         | Automotive Aerodynamics                                                                                                                                                     |
| Model Type:                                                                                            | Scalable Multi-Scale MeshGraphNet                                                                                                                                            |
| Intended User:                                                                                         | Accelerating Computational Fluid Dynamics (CFD) predictions using AI.                                                                                                                               |
| Output:                                                                                                | Tensor (4 variables - pressure and wall shear stress components on vehicle surface).                                                                                                                      |
| Describe how the model works:                                                                          | The X-MeshGraphNet (X-MGN) is a scalable, multi-scale extension of MeshGraphNet designed for fast physics simulation. Its architecture features three technical pillars: Custom Graph Construction directly from CAD files (e.g., STLs) via point clouds and $k$-nearest neighbors (KNN); Scalable Partitioning of large graphs with halo regions, where gradient aggregation ensures the training is mathematically equivalent to processing the full graph; and a Multi-Scale approach that refines graph resolution to efficiently capture long-range interactions.                                                    |
| Technical Limitations:                                                                                 | The model may perform poorly for vehicle geometries significantly different from the training data or for flow conditions outside the training dataset.  |
| Verified to have met prescribed NVIDIA quality standards:                                              | Yes                                                                                                                                                                                                       |
| Performance Metrics:                                                                                   | Surface prediction Root Mean Square Error (RMSE), Drag coefficient error percentage                                                                                                                                |
| Potential Known Risks:                                                                                 | This model may inaccurately predict aerodynamic fields for vehicle designs outside the training distribution.                                                                                             |
| Licensing:                                                                                             |  Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). |