Add model card README

README.md

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-license: other
-license_name: nvidia-open-model-license
-license_link: >-
-  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/
----
+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/
+---
+
+# Model Overview
+
+## Description
+
+XMeshGraphNet-DrivAerML is a pre-trained AI model for automotive external aerodynamics. This model has been trained using the [DrivAerML dataset](https://huggingface.co/datasets/neashton/drivaerml), that are LES simulations of road-cars of varying geometries. This pre-trained model works by taking the input from a single DrivAerML STL (Standard Tessellation Language)  geometry and evaluates a solution on the surface of the vehicle.
+
+This model is ready for commercial use.
+
+## License/Terms of Use:
+
+GOVERNING TERMS: The NIM container 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/).
+
+## Deployment Geography:
+
+Global
+
+## Use Case:
+
+Computational Fluid Dynamics (CFD) engineers accelerating automotive external aerodynamics with AI.
+
+## Release Date:  
+
+
+## Reference(s)
+
+* [Codebase](https://github.com/NVIDIA/physicsnemo/tree/main/examples/cfd/external_aerodynamics/xaeronet/surface) <br>
+
+* [Paper](https://arxiv.org/pdf/2411.17164) <br>
+
+## Model Architecture
+
+**Architecture Type:** Graph Neural Network with message passing blocks, 
+fully connected blocks, and partitioning with halo. <br>
+
+**Network Architecture:** 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.<br>
+
+** Number of model parameters: 12M
+
+## Input
+
+**Input Type(s):** Surface mesh (STL nodes and face connectivities) (3D) <br>
+**Input Format(s):** PyTorch Tensor / NumPy array <br>
+**Input Parameters:** 
+- Surface mesh (STL) coordinates (M, 3), where M is the number of cells in the surface mesh
+- Surface mesh normals (M, 3) <br>
+
+**Other Properties Related to Input:** None <br>
+
+## Output
+
+**Output Type(s):** Point cloud <br>
+**Output Format:** PyTorch Tensor / NumPy array <br>
+**Output Parameters:** 
+- Surface pressure (M, 1)
+- Wall shear stress (M, 3) <br>
+
+**Other Properties Related to Output:** The outputs are non-dimensionalized, and then normalized using mean and standard deviation calculated from the training dataset.
+<br>
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Software Integration
+**Runtime Engine(s):** PyTorch <br>
+**Supported Hardware Microarchitecture Compatibility:** <br>
+* NVIDIA Ampere <br>
+* NVIDIA Blackwell <br>
+* NVIDIA Hopper <br>
+* NVIDIA Turing <br>
+
+**Supported Operating System(s):**
+* Linux <br>
+
+## Model Version(s)
+
+**Model version:** 1.0.0  <br>
+
+# Training and Evaluation Datasets:
+
+The DrivAerML dataset is used for training and evaluation, which is a publicly available, high-fidelity dataset comprising aerodynamic data for 500 parametrically morphed variants of the DrivAer notchback vehicle. The dataset was generated using hybrid RANSLES (HRLES), a scale-resolving CFD method, which provides time-averaged quantities for each variant. The available data includes surface pressure, wall shear stress, and flow-field quantities, provided in formats compatible with mesh-based analysis (.vtp for surface data and .vtu for flow-field data).10% of the samples are used as the test set, with 20% of the test set consisting of out-of-distribution samples based on drag coefficients. These samples represent extreme cases with the lowest and highest drag coefficients in the entire dataset, which remain unseen by the model during training.
+
+## Training Dataset:
+
+**Data Modality:**
+
+- Other: Mesh
+
+**Training Data Size:**
+
+- 436 files in VTP format that contain meshes and corresponding physical quantities
+
+**Link:** [DrivAerML Dataset](https://arxiv.org/abs/2408.11969)
+
+**Data Collection Method by dataset:**
+
+- Synthetic CFD Simulation
+
+**Labeling Method by dataset:**
+
+- Automated
+
+**Properties:**
+The data is a simulation/synthetic dataset generated using the
+[OpenFOAM CFD solver](https://www.openfoam.com/news/main-news/openfoam-v2206)
+to generate flow fields such as velocity and pressure for different car geometries
+for the same boundary condition configuration as used to generate the training set.
+
+## Evaluation Dataset:
+
+**Link:** [DrivAerML Dataset](https://arxiv.org/abs/2408.11969)
+
+**Data Collection Method by dataset:**
+
+- Synthetic CFD Simulation
+
+**Labeling Method by dataset:**
+
+- Automated
+
+**Properties:**
+Validation split from DrivAerML dataset with vehicle geometries held out from
+training. The full DrivAerML dataset is split as 90% for training
+and 10% for validation.
+
+# Inference:
+
+**Engine:** PyTorch
+
+**Test Hardware:**
+* A100 <br>
+* H100 <br>
+* L40S <br>
+* RTX PRO 6000 Blackwell <br>
+
+## Ethical Considerations:
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established
+policies and practices to enable development for a wide array of AI applications.
+When downloaded or used in accordance with our terms of service, developers should work
+with their supporting model team to ensure this model meets requirements for the
+relevant industry and use case and addresses unforeseen product misuse.
+
+For more detailed information on ethical considerations for this model, please see the Model Card++ subcards: [Bias](https://huggingface.co/nvidia/xmgn_drivaerml_surface/blob/main/bias.md), [Explainability](https://huggingface.co/nvidia/xmgn_drivaerml_surface/blob/main/explainability.md), [Privacy](https://huggingface.co/nvidia/xmgn_drivaerml_surface/blob/main/privacy.md), and [Safety & Security](https://huggingface.co/nvidia/xmgn_drivaerml_surface/blob/main/safety.md).
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).