| | 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/). | | |