--- frameworks: - "" language: - en license: apache-2.0 tags: - OneScience - UNO - neural-operator - computational-fluid-dynamics - Navier-Stokes tasks: [] ---

UNO

# Model Overview UNO (U-shaped Neural Operator) combines the multiscale encoder–decoder structure of U-Net with the spectral convolutions of a Fourier Neural Operator. It learns PDE solution operators across multiple spatial scales while preserving local flow-field details through skip connections. This model package targets Navier–Stokes time-series prediction on two-dimensional regular grids. By default, it takes the first 10 time steps as input and predicts the subsequent 10 autoregressively. Paper: U-NO: U-shaped Neural Operators https://arxiv.org/abs/2204.11127 # Repository Overview This repository is a minimal, self-contained, runnable UNO model package maintained by OneScience for ModelScope downloads, automated OneCode execution, and rapid local validation. Supported capabilities: - Train a two-dimensional UNO model from a YAML configuration - Perform multistep autoregressive prediction of Navier–Stokes flow fields - Compute relative L2 errors and save predicted tensors and visualizations - Override the sample count, temporal window, spatial downsampling, and model size from the command line - Run on a CPU, GPU, or DCU Unsupported capabilities: - Pretrained weights are not bundled - The approximately 394 MiB raw Navier–Stokes data file is not bundled - The standalone model includes only the two-dimensional UNO implementation required by this example; the general-purpose OneScience 1D and 3D components are not included # Use Cases | Use Case | Description | | :---: | :--- | | Flow-field time-series prediction | Autoregressively predict future Navier–Stokes states from historical vorticity fields | | Neural operator training | Evaluate the combination of Fourier spectral convolutions and a U-shaped multiscale architecture | | CFD surrogate modeling | Learn mappings from historical to future fields on regular grids | | Pipeline validation | Validate training and inference with a small sample set and a single epoch | # File Structure | Path | Purpose | Notes | | :--- | :--- | :--- | | `README.md` | Project documentation | English | | `conf/config.yaml` | Data, model, training, and output configuration | Paths are resolved relative to the model package root | | `model/uno.py` | Standalone two-dimensional UNO model | Does not depend on `onescience.modules` | | `scripts/common.py` | Shared configuration, device, metric, and autoregressive utilities | Used by both training and inference | | `scripts/train.py` | Training and validation script | Saves the checkpoint with the best relative L2 error | | `scripts/inference.py` | Inference, evaluation, and visualization script | Loads `weight/*.pt` | | `data/` | Navier–Stokes data directory | Stores the benchmark `.mat` file | | `weight/` | Model weight directory | Checkpoints are written automatically during training | | `result/` | Inference result directory | Created automatically on first inference | # Usage ## 1. OneCode Use the online OneCode environment for an intelligent, one-click AI for Science (AI4S) programming experience: [Launch OneCode for one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) ## 2. Manual Setup **Hardware Requirements** - A GPU or DCU is recommended for full training. - A CPU can be used for pipeline validation with a reduced model and dataset, but full training will be slow. - DCU users must install DTK and a PyTorch environment compatible with the target cluster. ### Download the Model Package ```bash modelscope download --model OneScience/UNO --local_dir ./UNO cd UNO ``` ### Set Up the Runtime Environment **DCU Environment** ```bash # Activate DTK and Conda first conda create -n onescience311 python=3.11 -y conda activate onescience311 # Installation with uv is also supported pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai ``` **GPU Environment** ```bash # Activate Conda first conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 conda activate onescience311 # Installation with uv is also supported pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai ``` ## 3. Quick Start ### Prepare the Data Download the following file from the Transolver `PDE-Solving-StandardBenchmark`: ```text NavierStokes_V1e-5_N1200_T20.mat ``` Place the file in `data/`: ```text data/ NavierStokes_V1e-5_N1200_T20.mat ``` Data downloads and documentation: - https://github.com/thuml/Transolver/tree/main/PDE-Solving-StandardBenchmark - https://drive.google.com/drive/folders/1UnbQh2WWc6knEHbLn-ZaXrKUZhp7pjt- The OneScience community also provides the training data. Download it with the following command and verify that the data path in `conf/config.yaml` is configured correctly: ```bash modelscope download --dataset OneScience/cfd_benchmark data/ns/NavierStokes_V1e-5_N1200_T20.mat --local_dir ./data ``` ### Training ```bash python scripts/train.py ``` The default checkpoint is saved to `weight/uno_navier_stokes.pt`. The training script is controlled entirely by `conf/config.yaml` and does not accept command-line configuration arguments. ### Model Weights This repository provides weights trained on the standard Navier–Stokes dataset in the `weight/` directory. ### Inference, Evaluation, and Visualization ```bash python scripts/inference.py ``` The script loads `weight/uno_navier_stokes.pt` by default and generates the following files under `result/`: ```text result/ prediction_sample.pt prediction_sample.png ``` The inference script is likewise controlled entirely by `conf/config.yaml` and does not accept command-line configuration arguments. The weight path is determined jointly by `training.weight_dir` and `training.checkpoint_name`; the output directory and number of saved samples are controlled by `inference.result_dir` and `inference.num_samples`, respectively. # Configuration `conf/config.yaml` contains five sections: - `common`: device and random seed - `datapipe`: data file, sample splits, temporal windows, downsampling, and DataLoader settings - `model`: hidden channels, Fourier modes, normalization, and spatial padding - `training`: optimizer, learning-rate schedule, early stopping, and weight directory - `inference`: inference result directory and number of saved samples The model automatically updates `in_dim` from `t_in * out_dim` in the configuration, so the model input dimension does not need to be synchronized manually when the history window changes. # Data Format The `.mat` file must contain a variable named `u` with the following standard shape: ```text [1200, 64, 64, 20] ``` | Dimension | Meaning | | --- | --- | | `1200` | Number of independent flow-field samples | | `64, 64` | Height and width of the two-dimensional regular grid | | `20` | Number of consecutive time steps | The data pipeline produces: - `pos`: `[H*W, 2]`, normalized two-dimensional coordinates - `x`: `[H*W, t_in*out_dim]`, historical states - `y`: `[H*W, t_out*out_dim]`, future states # Official OneScience Resources | Platform | OneScience 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 | # Citations and License - Rahman, M. A., Ross, Z. E., and Azizzadenesheli, K. U-NO: U-shaped Neural Operators. arXiv:2204.11127, 2022. - Li, Z. et al. Fourier Neural Operator for Parametric Partial Differential Equations. arXiv:2010.08895, 2020. - This model package is released under the Apache-2.0 license and retains attribution to the original paper and data sources.