Transolver: A Fast Transformer Solver for PDEs on General Geometries
Paper • 2402.02366 • Published
Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: TypeError
Message: Value.__init__() missing 1 required positional argument: 'dtype'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 700, in get_module
config_name: DatasetInfo.from_dict(dataset_info_dict)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
File "<string>", line 20, in __init__
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 170, in __post_init__
self.features = Features.from_dict(self.features)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1993, in from_dict
obj = generate_from_dict(dic)
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1574, in generate_from_dict
return {key: generate_from_dict(value) for key, value in obj.items()}
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1593, in generate_from_dict
return class_type(**{k: v for k, v in obj.items() if k in field_names})
TypeError: Value.__init__() missing 1 required positional argument: 'dtype'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ShapeNetCar
Dataset Description
The ShapeNetCar dataset comes from the paper Learning Three-dimensional Flow for Interactive Aerodynamic Design by Umetani and Bickel, published in ACM Transactions on Graphics (SIGGRAPH 2018). Based on three-dimensional car geometries from ShapeNet, the dataset uses CFD simulations to obtain velocity fields around the vehicles, surface pressure, and drag coefficients. It supports research on rapidly predicting aerodynamic physical fields and forces from three-dimensional geometry.
Supported Tasks
This standardized data repository is organized for the ShapeNetCar external flow field prediction task. It contains raw training data, preprocessed graph data, normalization statistics, linear regression utility code, paper figure and table data, and result comparison plots. It can be used for training, inference, evaluation, and visualization with the OneScience/Transolver-Car-Design model. This repository contains 889 car geometry samples grouped from param0 through param8. It retains the raw CFD arrays, meshes, and VTK files while also providing preprocessed samples for graph neural network models. Each preprocessed sample consists of node features, target physical fields, three-dimensional coordinates, a surface mask, and graph edge indices.
Dataset Format and Structure
In the preprocessed graph data, each preprocessed_data/param*/<sample_id>/ directory contains:
| File | shape | dtype | Description |
|---|---|---|---|
x.npy |
[num_nodes, 7] |
float64 |
Node input features, including position, signed distance function (SDF), normals, and other features |
y.npy |
[num_nodes, 4] |
float64 |
Target physical fields; the first three channels are velocity components and the last channel is pressure |
pos.npy |
[num_nodes, 3] |
float32 |
Three-dimensional node coordinates |
surf.npy |
[num_nodes] |
float64 |
Surface node mask |
edge_index.npy |
[2, num_edges] |
int64 |
Edge indices of the graph structure |
Normalization statistics files are located in stats/:
| File | shape | dtype | Description |
|---|---|---|---|
mean_in.npy |
[7] |
float32 |
Mean of the input features |
std_in.npy |
[7] |
float32 |
Standard deviation of the input features |
mean_out.npy |
[4] |
float32 |
Mean of the output physical quantities |
std_out.npy |
[4] |
float32 |
Standard deviation of the output physical quantities |
How to Use the Dataset
This dataset is compatible with the OneScience-Group/Transolver-Car-Design model. At runtime, scripts in the model package can organize the data from this repository under data/.
- Files and Download:
hf download --dataset OneScience-Group/ShapeNetCar --local-dir ./data
Official OneScience Information
| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
Citation and License
- This dataset is organized or converted from the ShapeNetCar three-dimensional aerodynamic flow field data released by Nobuyuki Umetani and Bernd Bickel. When using it, please cite the original ShapeNetCar paper: Learning Three-dimensional Flow for Interactive Aerodynamic Design
- If this dataset is used to reproduce the Transolver car aerodynamic design experiments, please also cite the original Transolver paper: Transolver: A Fast Transformer Solver for PDEs on General Geometries
- This repository retains source attribution and is organized for automated OneScience ModelScope execution scenarios. Before public distribution, please verify the license requirements of the upstream project.
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