nvidia/figconvnet_drivaerml_surface

FIGConvNet DrivAerML Surface

FIGConvNet DrivAerML Surface is a deep learning model for predicting surface aerodynamic fields on automotive geometries. The model predicts pressure and wall shear stress fields on 3D vehicle surface meshes for computational fluid dynamics (CFD) applications.

This model is available for commercial use.

License/Terms of Use:

Use of this model is governed by the NVIDIA Open Model Agreement.

Deployment Geography:

Global

Use Case:

Computational Fluid Dynamics (CFD) engineers accelerating automotive external aerodynamics with AI.

Release Date:

05/01/2026

Hugging Face: https://huggingface.co/nvidia/figconvnet_drivaerml_surface

Reference(s):

Code

Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics

Model Architecture:

Architecture Type: FIGConvNet uses a U-Net architecture with factorized implicit global convolutional layers.

Network Architecture: FIGConvUNet

Number of model parameters: 6,577,413

Input:

Input Type(s):

• Tensor (3D point cloud coordinates on vehicle surface)

Input Format(s): PyTorch Tensor

Input Parameters:

• Three Dimensional (3D) (batch, num_points, 3)

Other Properties Related to Input:

• Input point cloud represents vehicle surface geometry with coordinates normalized to the bounding box: x ∈ [-2.0, 2.0], y ∈ [-1.8, 1.8], z ∈ [-1.5, 2.6]
• Typical input size: 500,000 points per vehicle geometry

Output:

Output Type(s): Tensor (Surface aerodynamic fields)

Output Format: PyTorch Tensor

Output Parameters: Three Dimensional (3D) (batch, num_points, 4)

Other Properties Related to Output:

• Output channels: 1 pressure field + 3 wall shear stress components (x, y, z)
• Predictions correspond to time-averaged CFD simulation results

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

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Hopper
• NVIDIA Turing

Supported Operating System(s):

• Linux

Model Version(s):

Model Version: 1.0.0

Training, Testing, 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: 3D Point Cloud

Training Data Size:

• 436 files in VTP format that contain meshes and corresponding physical quantities

Link: DrivAerML Dataset

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 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

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
• H100

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 internal 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, Explainability, Privacy, and Safety & Security.

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