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'

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CFDBench

Dataset Description

CFDBench is a large-scale benchmark dataset for machine learning methods in computational fluid dynamics, designed to evaluate the generalization capabilities of neural operators under unseen boundary conditions, fluid properties, and geometries.

The dataset contains four classic CFD problems: lid-driven cavity flow (cavity), laminar pipe flow (tube), step dam-break flow (dam), and flow around a cylinder (cylinder). For each problem, subsets are organized by variations in boundary conditions (bc), geometry (geo), and fluid properties (prop).

Paper: CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

Supported Tasks

Scenario	Description
Flow field time-series prediction	Predict subsequent flow states from historical velocity fields.
CFD surrogate modeling	Learn the mapping from physical parameters, boundary conditions, or geometry to velocity field evolution.
Generalization evaluation	Evaluate model performance under unseen boundary conditions, fluid properties, and geometries.
Neural operator research	Provide unified CFD evaluation data for models such as FNO and Transformer.

Dataset Format and Structure

The data is organized by problem type, subset, and case:

data/<problem>/<subset>/case*/

Each case contains a physical parameter file and a two-dimensional velocity field time series:

File	Format	Description
`case.json`	JSON	Fluid properties, boundary conditions, or geometric parameters.
`u.npy`	NumPy NPY	Horizontal velocity component with shape `[T, 64, 64]` and dtype `float64`.
`v.npy`	NumPy NPY	Vertical velocity component with the same shape as `u.npy`.

Some cases also provide u.png and v.png as preview images; they are not required training data.

How to Use the Dataset

This dataset is compatible with the OneScience-Group/CFDBench model. Download the dataset and model:

hf download --dataset OneScience-Group/cfdbench --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

Luo, Y. et al. CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics, 2023.
This dataset is organized from the original CFDBench data and is licensed under the same Apache License 2.0.

Downloads last month: 12

Total file size:

16 GB

Paper for OneScience-Group/cfdbench

CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics

Paper • 2310.05963 • Published Sep 13, 2023 • 1