| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| tags: |
| - OneScience |
| - Kolmogorov-flow |
| - computational-fluid-dynamics |
| - turbulence |
| - vorticity |
| - neural-operator |
| - FactFormer |
| frameworks: |
| - NumPy |
| pretty_name: Kolmogorov Flow 2D Re1000 |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">Kolmogorov Flow 2D</span> |
| </strong> |
| </p> |
| |
| ## Dataset Description |
|
|
| This dataset contains time series of single-channel vorticity fields obtained from numerical simulations of two-dimensional Kolmogorov Flow. It can be used for turbulence time-series forecasting, neural operator training, partial differential equation surrogate modeling, and long-horizon autoregressive forecasting. |
|
|
| The data describes scalar vorticity fields over a two-dimensional periodic domain. The dataset contains 120 trajectories, each with 320 frames, a spatial resolution of `256 x 256`, a data type of `float32`, and a Reynolds number of `Re = 1000`. The dataset has been adapted for `OneScience-Group/FactFormer`. |
|
|
| ## Supported Tasks |
|
|
| | Scenario | Description | |
| |---|---| |
| | Multi-step flow field forecasting | Predict flow fields for multiple subsequent time steps from historical vorticity fields. | |
| | Autoregressive time-series modeling | Feed model outputs back into the input window for long-horizon rollouts. | |
| | Neural operator research | Compare operator architectures such as Transformer, FNO, UNO, and KNO. | |
| | Turbulence surrogate modeling | Learn the spatiotemporal evolution mappings produced by numerical solvers. | |
|
|
| ## Dataset Format and Structure |
|
|
| The main data file is in NumPy `.npy` format: |
|
|
| ```text |
| kf_2d_re1000_256_120seed.npy |
| ``` |
|
|
| The array shape is `[120, 320, 256, 256]`, with the dimensions representing trajectory, time, x-grid, and y-grid, respectively. The original file is approximately 9.38 GiB. On-demand access via `numpy.load(..., mmap_mode="r")` is recommended to avoid copying the entire array into memory. |
|
|
| The data contains only vorticity fields; it does not include velocity, pressure, forcing fields, or physical time-step information. |
|
|
| ## How to Use the Dataset |
|
|
| Download the dataset: |
|
|
| ```bash |
| hf download --dataset OneScience-Group/Kolmogorov_flow_2d --local-dir ./Kolmogorov_flow_2d |
| ``` |
|
|
| To use the dataset with FactFormer, set the data directory in `FactFormer/conf/config.yaml` to the download directory, and run: |
|
|
| ```bash |
| cd FactFormer |
| python scripts/train.py |
| python scripts/inference.py |
| ``` |
|
|
| By default, FactFormer downsamples the spatial resolution to `128 x 128` and uses the first 10 frames to predict the subsequent 16 frames. |
|
|
| ## 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 |
|
|
| - Recommended model: `OneScience/FactFormer`. |
| - FactFormer: Liu-Schiaffini et al., *FactFormer: Factorized Transformer for Modeling Long-Range Dependencies in PDE Surrogate Modeling*. |
| - The documentation and supporting scripts in this repository are licensed under Apache-2.0. Before publicly distributing or redistributing the numerical data, verify its upstream source and licensing requirements. |
|
|
