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Add explainability subcard

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+ | Field | Response |
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+ |:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | Intended Application & Domain: | Automotive Aerodynamics |
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+ | Model Type: | Factorized Implicit Global Convolutional U-Net |
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+ | Intended User: | Automotive engineers and researchers accelerating CFD predictions using AI. |
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+ | Output: | Tensor (4 variables - pressure and wall shear stress components on vehicle surface). |
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+ | 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. |
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+ | Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | N/A |
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+ | 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. |
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+ | Verified to have met prescribed NVIDIA quality standards: | Yes |
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+ | Performance Metrics: | Relative Root Mean Square Error (RRMSE), Drag coefficient. |
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+ | Potential Known Risks: | This model may inaccurately predict aerodynamic fields for vehicle designs outside the training distribution. |
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+ | Licensing: | The model is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). |