Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
Paper • 2302.10803 • 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.
EAGLE
Dataset Description
The EAGLE dataset is sourced from an ICLR 2023 paper and is designed for predicting two-dimensional unsteady turbulent flows on unstructured dynamic meshes. The data describes the velocity and pressure fields produced by interactions between a moving flow source and various ground geometries. It contains three types of geometric configurations: Cre, Spl, and Tri.
Paper: EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
Supported Tasks
| Scenario | Description |
|---|---|
| Turbulent-flow time-series prediction | Predict subsequent velocity and pressure fields from historical mesh states. |
| Dynamic mesh modeling | Learn fluid evolution on unstructured meshes. |
| Geometric generalization evaluation | Compare model performance across different ground geometries. |
| Mesh Transformer research | Provide data for models such as OneScience/EagleMeshTransformer. |
Dataset Format and Structure
Samples are organized by geometry type, case, and run number:
data/Eagle_dataset/<geometry>/<case>/<run>/
Each sample mainly contains:
| File | Contents | Description |
|---|---|---|
sim.npz |
pointcloud, mask, VX, VY, PS, PG |
Node coordinates, types, velocity, and pressure-related fields. |
triangles.npy |
[time, cells, 3] |
Triangular mesh cell indices. |
constrained_kmeans_*.npy |
Node clustering indices | Multiple clustering scales are provided; the default model uses 40. |
The dataset provides lists of training, validation, and test samples. Refer to metadata/schema.yaml for the shape and dtype of each field.
How to Use the Dataset
This dataset is compatible with the OneScience-Sugon/EagleMeshTransformer model. Download the dataset and model:
hf download --dataset OneScience-Sugon/eagle --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
- Original EAGLE paper: EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers
- This repository retains information about the data source and organizes the data for automated runs on OneScience ModelScope. Before public distribution or redistribution, confirm the licensing requirements of the upstream project.
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