| Field                                                                                                  | Response                                                                                                                                                                                                  |
|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended Application & Domain:                                                                         | Automotive Aerodynamics                                                                                                                                                                                   |
| Model Type:                                                                                            | Factorized Implicit Global Convolutional U-Net                                                                                                                                                            |
| Intended User:                                                                                         | Automotive engineers and researchers accelerating CFD predictions using AI.                                                                                                                               |
| Output:                                                                                                | Tensor (4 variables - pressure and wall shear stress components on vehicle surface).                                                                                                                      |
| Describe how the model works:                                                                          | FIGConvUNet uses factorized implicit global convolutions in a U-Net architecture to predict aerodynamic surface fields from 3D point cloud geometry.                                                      |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  | N/A                                                                                                                                                                                                       |
| Technical Limitations:                                                                                 | The model may perform poorly for vehicle geometries significantly different from the training data or for flow conditions outside the training regime. Physical consistency is not explicitly enforced.   |
| Verified to have met prescribed NVIDIA quality standards:                                              | Yes                                                                                                                                                                                                       |
| Performance Metrics:                                                                                   | Relative Root Mean Square Error (RRMSE), Drag coefficient.                                                                                                                                                |
| 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/). |