Add model card README

README.md

@@ -5,38 +5,36 @@ license_link: >-
   https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/
 ---
 
-# Model Overview
-
-## Description
+# XMeshGraphNet DrivAerML
 
 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:
+### 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:
+### Deployment Geography:
 
 Global
 
-## Use Case:
+### Use Case:
 
 Computational Fluid Dynamics (CFD) engineers accelerating automotive external aerodynamics with AI.
 
-## Release Date:  
+### Release Date:
 
 
-## Reference(s)
+## 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
+## Model Architecture:
 
 **Architecture Type:** Graph Neural Network with message passing blocks, 
 fully connected blocks, and partitioning with halo. <br>
@@ -52,7 +50,7 @@ efficiently capture long-range interactions.<br>
 
 ** Number of model parameters: 12M
 
-## Input
+## Input:
 
 **Input Type(s):** Surface mesh (STL nodes and face connectivities) (3D) <br>
 **Input Format(s):** PyTorch Tensor / NumPy array <br>
@@ -62,7 +60,7 @@ efficiently capture long-range interactions.<br>
 
 **Other Properties Related to Input:** None <br>
 
-## Output
+## Output:
 
 **Output Type(s):** Point cloud <br>
 **Output Format:** PyTorch Tensor / NumPy array <br>
@@ -86,7 +84,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 **Supported Operating System(s):**
 * Linux <br>
 
-## Model Version(s)
+## Model Version(s):
 
 **Model version:** 1.0.0  <br>
 
@@ -137,7 +135,7 @@ 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:
+## Inference:
 
 **Engine:** PyTorch