Datasets:
ArXiv:
License:
| license: apache-2.0 | |
| #User-Defined Tags | |
| tags: | |
| - CFDBench | |
| - computational fluid dynamics | |
| - neural operator | |
| language: | |
| - en | |
| - zh | |
| <p align="center"> | |
| <strong> | |
| <span style="font-size: 30px;">CFDBench</span> | |
| </strong> | |
| </p> | |
| ## 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](https://arxiv.org/abs/2310.05963) | |
| ## 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: | |
| ```text | |
| 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: | |
| ```bash | |
| 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. | |