| --- |
| language: |
| - en |
| tags: |
| - OneScience |
| - fluid dynamics |
| - large-scale fluid dynamics benchmark |
| frameworks: PyTorch |
| --- |
| |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">CFDBench</span> |
| </strong> |
| </p> |
| |
| # Model Overview |
|
|
| CFDBench is a large-scale benchmark developed by researchers at Tsinghua University to evaluate machine-learning methods for computational fluid dynamics. It focuses on model generalization across different boundary conditions, fluid properties, and geometric configurations. |
|
|
| Paper: [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963) |
|
|
| # Model Description |
| CFDBench is built from several representative computational fluid dynamics datasets spanning diverse boundary conditions, fluid properties, and geometries. It evaluates both flow-field prediction performance and the generalization capabilities of machine-learning methods. |
|
|
|
|
| # Use Cases |
|
|
| | Use Case | Description | |
| | :---: | :---: | |
| | CFD surrogate benchmarking | Compare the flow-field prediction capabilities of neural operators and other deep-learning models using consistent data splits and metrics | |
| | Autoregressive flow evolution | Predict subsequent two-dimensional velocity fields step by step from the current grid-based field and assess error accumulation over multiple steps | |
| | Non-autoregressive field queries | Predict velocity directly at target locations from operating parameters and spatiotemporal coordinates, enabling evaluation over long time horizons | |
|
|
|
|
| ## Supported Models |
|
|
| | Type | `root.model.name` | Training Entry Point | |
| | :--- | :--- | :--- | |
| | Non-autoregressive | `ffn` | `python scripts/train.py` | |
| | Non-autoregressive | `deeponet` | `python scripts/train.py` | |
| | Autoregressive | `auto_ffn` | `python scripts/train_auto.py` | |
| | Autoregressive | `auto_deeponet` | `python scripts/train_auto.py` | |
| | Autoregressive | `auto_edeeponet` | `python scripts/train_auto.py` | |
| | Autoregressive | `auto_deeponet_cnn` | `python scripts/train_auto.py` | |
| | Autoregressive | `resnet` | `python scripts/train_auto.py` | |
| | Autoregressive | `unet` | `python scripts/train_auto.py` | |
| | Autoregressive | `fno` | `python scripts/train_auto.py` | |
|
|
| # 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. |
| - A CPU can be used for import checks and small-scale pipeline validation, but full training and inference will be slow. |
| - DCU users must install DTK in advance. DTK 25.04.2 or later, or the OneScience-recommended version for the target cluster, is recommended. |
|
|
| ### Download the Model Package |
| ``` |
| hf download OneScience-Group/CFDBench --local-dir ./CFDBench |
| cd CFDBench |
| ``` |
| ### 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 |
| ``` |
|
|
| ### Training Data |
|
|
| The OneScience community provides the `cfdbench` dataset for training. Download it with the command below and verify that the data path in `config/config.yaml` is configured correctly: |
|
|
| ```bash |
| hf download --repo-type dataset OneScience-Group/cfdbench --local-dir ./data |
| ``` |
|
|
| ### Training |
| **Autoregressive Training** |
|
|
| The default configuration sets `root.model.name: fno`, so use the autoregressive training entry point: |
|
|
| ```bash |
| python scripts/train_auto.py |
| ``` |
|
|
|
|
| To select another autoregressive model, edit `conf/config.yaml`: |
|
|
| ```yaml |
| root: |
| model: |
| name: "auto_ffn" |
| ``` |
|
|
| **Non-autoregressive Training** |
| ```bash |
| python scripts/train.py --model deeponet |
| ``` |
| ### Model Weights |
| This repository will provide pretrained CFDBench model weights in the `weights/` directory. The weights will be uploaded soon. |
|
|
| ### Inference and Visualization |
|
|
| The inference script automatically selects the task type from the current model name and reads the following path by default: |
|
|
| ```text |
| ./weight/<model.name>.pt |
| ``` |
|
|
| Default FNO inference and visualization: |
|
|
| ```bash |
| python scripts/inference.py |
| python scripts/result.py |
| ``` |
|
|
| Non-autoregressive models can likewise be selected through a command-line argument: |
|
|
| ```bash |
| python scripts/inference.py --model ffn |
| python scripts/result.py --model ffn |
| ``` |
| # 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 |
|
|
| - Original CFDBench paper: [CFDBench: A Large-Scale Benchmark for Machine Learning Methods in Fluid Dynamics](https://arxiv.org/abs/2310.05963) |
| - Original CFDBench code repository: https://github.com/luo-yining/CFDBench |
| - This repository retains source attribution and has been adapted for automated execution through OneScience and ModelScope. Before public redistribution, verify the applicable licensing requirements of the upstream project. |
|
|
