Transolver DrivAerML
Transolver DrivAerML is a Transformer-based surrogate model for automotive
external aerodynamics simulations. It introduces Physics-Attention, which
adaptively decomposes the computational domain into learnable physical state
slices, enabling efficient attention over physics-aware tokens rather than
raw mesh points. The model predicts surface pressure and wall shear stress
fields, as well as volumetric velocity and pressure fields on 3D vehicle
geometries 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
Reference(s):
Model Architecture:
Architecture Type: Transformer with Physics-Attention over learnable
physical state slices.
Network Architecture: Transolver introduces Physics-Attention, which
replaces standard self-attention by first learning soft assignments of mesh
points to M physics-aware slice tokens via a linear projection and Softmax.
Each of the M tokens is computed as a weighted average of mesh point features.
Standard multi-head attention is then applied over the M tokens rather than
all N mesh points, achieving O(N) complexity since M ≪ N. Updated token
representations are broadcast back to mesh points via the learned slice
weights. Each Transolver layer applies LayerNorm, Physics-Attention with
residual, LayerNorm, and a feed-forward block with residual. The model
comprises 8 such layers. This formulation is grounded theoretically as a
learnable integral operator approximation over the PDE domain.
Number of model parameters: 10M
Input:
Input Type(s):
- Tensor (3D point cloud coordinates on vehicle surface and volume)
Input Format(s): PyTorch Tensor
Input Parameters:
- Point coordinates and geometry features (N, C_g) for surface and volume
points, linearly embedded to a common channel dimension C
Other Properties Related to Input:
- Input point cloud represents vehicle surface and volume geometry
- Geometry features are concatenated with optional observed physical
quantities and linearly projected to the model's working dimension
Output:
Output Type(s): Tensor (Surface and volume aerodynamic fields)
Output Format: PyTorch Tensor
Output Parameters:
- Surface: pressure (M_s, 1), wall shear stress (M_s, 3)
- Volume: velocity (M_v, 3), pressure (M_v, 1)
Other Properties Related to Output:
- Outputs are normalized using statistics computed from the training dataset
- Drag and lift coefficients can be derived via surface integration of
pressure and wall shear stress predictions
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):
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
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 RANS/LES (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). 48 samples (~10%) are used as the test set,
with approximately 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. Models are trained for up to 500 epochs
on a single NVIDIA GB200 node using the Muon optimizer.
Training Dataset:
Data Modality:
- Other: 3D Point Cloud (surface and volume)
Training Data Size:
- 436 files in VTP format (surface meshes) and VTU format (volume flow fields)
with corresponding physical quantities
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
The data is a simulation/synthetic dataset generated using hybrid RANS/LES
scale-resolving CFD simulations, providing time-averaged surface and volumetric
flow fields for different car geometries. Each case contains approximately
150 million volume elements and 10 million surface elements.
Testing Dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
Properties:
Test split from DrivAerML dataset with vehicle geometries held out from training.
48 samples (~10%) are used as the test set, with approximately 20% consisting of
out-of-distribution samples based on drag coefficients.
Evaluation Dataset:
Data Collection Method by dataset:
Labeling Method by dataset:
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:
Acceleration Engine: PyTorch
Test Hardware:
Ethical Considerations:
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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:
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Explainability,
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