| | Field | Response | | |
| |:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | |
| | Intended Task/Domain: | Automotive Aerodynamics | | |
| | Model Type: | Transformer with Physics-Attention (Transolver) | | |
| | Intended Users: | Automotive engineers and researchers accelerating Computational Fluid Dynamics (CFD) predictions using AI. | | |
| | Output: | Tensor (surface pressure and wall shear stress; volumetric velocity and pressure fields). | | |
| | Describe how the model works: | Transolver introduces Physics-Attention, which decomposes the mesh domain into M learnable physical state slices by learning soft point-to-slice assignments. Multi-head attention is applied over the M slice tokens rather than all N mesh points (O(N) complexity), then results are broadcast back to mesh points. Each of 8 Transolver layers applies this Physics-Attention with residual connections and a feed-forward block, effectively approximating a learnable integral operator over the PDE domain. | | |
| | Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable | | |
| | Technical Limitations & Mitigation: | The number of slices M requires task-specific tuning; over-large M can degrade performance through over-fragmentation. The model may perform poorly for vehicle geometries significantly different from the training data or for flow conditions outside the training distribution. | | |
| | Verified to have met prescribed NVIDIA quality standards: | Yes | | |
| | Performance Metrics: | Surface and volume prediction relative L1 error; drag and lift coefficient R² | | |
| | 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/). | | |