| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - fluid-dynamics |
| - burgers-equation |
| - pytorch |
| library_name: pytorch |
| --- |
| |
| <p align="center"><strong><span style="font-size: 30px;">CFO-Burgers-Flow</span></strong></p> |
|
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| # Model Introduction |
|
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| CFO-Burgers-Flow predicts continuous-time trajectories of the one-dimensional Burgers equation from an initial state. |
|
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| Paper: [CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators](https://arxiv.org/pdf/2512.05297). |
|
|
| # Model Description |
|
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| This package reproduces the paper's Table 1 Burgers experiment with 100% time sampling and the Quintic CFO method. A time-conditioned 1D U-Net learns a continuous-time velocity field, and RK4 integrates the predicted trajectory. |
|
|
| ## Intended Use |
|
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| | Task | Description | |
| | --- | --- | |
| | Burgers forecasting | Predict a full space-time trajectory from an initial field | |
| | PDE surrogate research | Study continuous-time neural operators | |
|
|
| # Usage |
|
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| ## 1. OneCode |
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| [Open the OneCode environment](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home). |
|
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| ## 2. Manual Setup |
|
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| **Hardware requirements** |
|
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| A GPU or DCU is recommended. CPU inference is possible but slower. |
|
|
| ### Download the Model Package |
|
|
| ```bash |
| hf download OneScience-Group/CFO-Burgers-Flow --local-dir ./CFO-Burgers-Flow |
| cd CFO-Burgers-Flow |
| ``` |
|
|
| ### Set Up the Runtime Environment |
|
|
| **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 scipy 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 scipy pyyaml matplotlib |
| ``` |
|
|
| ### Training Data |
|
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| The [OneScience/CFO-Burgers dataset on ModelScope](https://www.modelscope.cn/datasets/OneScience/CFO-Burgers) provides the original `Burger.mat` and `DATA_MANIFEST.json`. Place both files in `data/`. The original data came from the paper authors' [CFO data download page](https://drive.google.com/drive/folders/1tz0FQ8qqbLMxu_WPlAMHZmLMr2sBv2oE). The file has 1200 trajectories; the model uses 1000/100/100 train/validation/test trajectories, with a fixed split seed of 43. |
|
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| This Hugging Face repository contains the **model only**, not a Hugging Face dataset mirror. Do not use a Hugging Face dataset download command until such a dataset repository exists. |
|
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| ### Training |
|
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| From the model package root, run: |
|
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| ```bash |
| python scripts/train.py |
| ``` |
|
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| The configured run uses 60,000 training steps. |
|
|
| ### Checkpoint |
|
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| The trained checkpoint is `weight/best_model.pth` (7,308,542 bytes; SHA256 `7287ce3dddb7f620f75d7ab6a9dc8ce9fa313b67e87566e8c19841effc79e42b`). The reproduced test trajectory relative L2 is `0.00347142`; the paper's Table 1 reports `0.00589 +/- 0.00079` across multiple runs, so these are not identical statistical settings. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
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| # Official OneScience Information |
|
|
| | 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 | |
|
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| # Citation and License |
|
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| Paper: [CFO](https://arxiv.org/pdf/2512.05297). Official implementation: [CFO_official](https://github.com/shannon-hou/CFO_official). The original data's redistribution terms should be checked separately from the official code license. |
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