| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - OneScience |
| - PDE |
| - CFD |
| - neural-operator |
| - PDEBench |
| frameworks: PyTorch |
|
|
| datasets: |
| - OneScience/pdenneval |
| --- |
| <p align="center"> |
| <strong> |
| <span style="font-size: 30px;">PDENNEval</span> |
| </strong> |
| </p> |
| |
| # Model Overview |
| PDENNEval is a comprehensive benchmark developed by researchers at Sun Yat-sen University for evaluating neural-network methods for solving partial differential equations. It enables systematic comparison of function-learning and operator-learning approaches across a broad range of PDE tasks. |
|
|
| Paper: [PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://www.ijcai.org/proceedings/2024/0573.pdf) |
|
|
| # Model Description |
| PDENNEval provides a unified framework for evaluating neural-network PDE solvers. It contains 16 datasets generated with high-accuracy conventional scientific computing methods, covering 19 PDE problem classes from fluid dynamics, materials science, finance, electromagnetics, and other domains. The benchmark evaluates the solution accuracy, computational efficiency, and robustness of 12 neural-network methods. |
|
|
| ## Use Cases |
|
|
| | Use Case | Description | |
| | --- | --- | |
| | PDE neural-operator validation | Complete a minimal FNO training and inference workflow using PDEBench-style HDF5 data | |
| | Neural PDE method development | Extend the model collection in `model/`, which already includes FNO, DeepONet, PINO-FNO, UNO, MPNN, and UNet definitions | |
| | ModelScope/OneCode execution | Download the standalone model package, install its dependencies, and run the provided scripts | |
| | Rapid pipeline checks | Use `fake_data.py` and `smoke_models.py` to verify the data, models, and scripts | |
|
|
| # 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 |
|
|
| ```bash |
| hf download --model OneScience-Sugon/PDENNEval --local-dir ./PDENNEval |
| cd PDENNEval |
| ``` |
|
|
| ### 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 `pdenneval` dataset for training. Download it with the command below and verify that the data path in `conf/config.yaml` is configured correctly: |
|
|
| ```bash |
| hf download --dataset OneScience-Sugon/pdenneval --local-dir ./data |
| ``` |
|
|
| ### Training |
|
|
| ```bash |
| python scripts/train.py |
| ``` |
|
|
| ### Model Weights |
| This repository will provide weights trained on the `pdenneval` dataset in the `weights/` directory. The weights will be uploaded soon. |
|
|
| ### Inference |
|
|
| ```bash |
| python scripts/inference.py |
| ``` |
|
|
| By default, inference loads `weight/best_model.pt` and writes `.npz` results to `result/output/`. |
|
|
| ### Evaluation and Visualization |
|
|
| ```bash |
| python scripts/result.py |
| ``` |
|
|
| # 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 PDENNEval paper: [PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs](https://www.ijcai.org/proceedings/2024/0573.pdf). |
| - This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents. |
|
|
