| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - computational-fluid-dynamics |
| - shallow-water-equations |
| frameworks: PyTorch |
| --- |
| <p align="center"><strong><span style="font-size: 30px;">FluxNet-ShallowWater-LAP</span></strong></p> |
|
|
| # Model Introduction |
|
|
| FluxNet-ShallowWater-LAP reproduces the two-dimensional shallow-water FluxNet-LAP pushforward experiment in the [FluxNet paper](https://arxiv.org/pdf/2602.01941). It forecasts water depth and two momentum fields on a 64x64 grid. |
|
|
| # Model Description |
|
|
| The model takes `(h, mx, my)` and predicts the next state. The configuration uses 64 channels, six residual blocks, 5x5 kernels, a 3x3 transport neighborhood and five-step pushforward training. |
|
|
| ## Use Cases |
|
|
| | Use case | Description | |
| | --- | --- | |
| | Shallow-water forecasting | Forecast depth and momentum on a regular grid | |
| | Simulation acceleration | Compare surrogate rollouts against numerical reference trajectories | |
|
|
| # Usage |
|
|
| ## 1. OneCode |
|
|
| [Open OneCode](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) |
|
|
| ## 2. Manual Setup |
|
|
| **Hardware requirements** |
|
|
| - Training and inference require a Slurm-allocated HIP/DCU compute node; there is no silent CPU fallback. |
| - The formal run used one DCU, eight CPU cores and 30 GB of host memory. GPU execution was not validated. |
|
|
| ### Download the Model |
|
|
| ```bash |
| hf download OneScience-Group/FluxNet-ShallowWater-LAP --local-dir ./FluxNet-ShallowWater-LAP |
| cd FluxNet-ShallowWater-LAP |
| ``` |
|
|
| ### Install Dependencies |
|
|
| **DCU environment** |
|
|
| ```bash |
| conda create -n onescience311 python=3.11 -y |
| conda activate onescience311 |
| pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| pip install numpy h5py PyYAML matplotlib |
| ``` |
|
|
| **GPU environment** |
|
|
| ```bash |
| 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 |
| pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai |
| pip install numpy h5py PyYAML matplotlib |
| ``` |
|
|
| The GPU commands describe dependency installation only; the provided scripts were validated on Slurm HIP/DCU. |
|
|
| ### Training Data |
|
|
| The [OneScience/FluxNet-ShallowWater dataset on ModelScope](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater) contains 50 training, 20 validation, 50 short-test and 50 long-test trajectories produced with the paper's official numerical generator. Each HDF5 trajectory contains `h`, `mx`, `my` and `t`. The dataset is not mirrored on Hugging Face. Download it into the model directory: |
|
|
| ```bash |
| modelscope download --dataset OneScience/FluxNet-ShallowWater --local_dir ./data |
| ``` |
|
|
| Run commands from the package root. `data.root` in `config/config.yaml` defaults to `./data`. |
|
|
| ### Training |
|
|
| On a Slurm-allocated DCU node: |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| The full run trains for 300 epochs and writes `weight/best_model.pth`. Back up the included `best_model.pth` and `last_model.pth` before retraining if they must be preserved. |
|
|
| ### Trained Weights |
|
|
| The included checkpoint is the best validation-MSE model at epoch 283 of a completed 300-epoch run. `weight/best_model.pth` is 15636928 bytes; SHA256: `47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70`. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| Outputs are written to `results/`. At the long-horizon endpoint (4.8 time units), MAE for `(h, mx, my)` is `(1.5568e-3, 1.9634e-3, 1.7648e-3)`; averaging over all predicted steps gives `(1.4063e-3, 1.8558e-3, 1.7632e-3)`. The paper's Table 3 two-times extrapolation means are `(1.75e-3, 2.38e-3, 2.30e-3)`. This run uses one seed rather than the paper's five independently trained models, so the figures are not a strict like-for-like reproduction. |
|
|
| # OneScience Repositories |
|
|
| | Platform | 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 |
|
|
| - Paper: [FluxNet](https://arxiv.org/pdf/2602.01941). |
| - [Original public repository](https://github.com/Lan-zs/FluxNet); [public dataset and its provenance](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater). This model package is an independent reproduction. |
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|
