license: apache-2.0
language:
- en
tags:
- OneScience
- PDE
- CFD
- neural-operator
- PDEBench
frameworks: PyTorch
datasets:
- OneScience/pdenneval
PDENNEval
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
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
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 --model OneScience-Sugon/PDENNEval --local-dir ./PDENNEval
cd PDENNEval
Set Up the Runtime Environment
DCU Environment
# 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
# 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:
hf download --dataset OneScience-Sugon/pdenneval --local-dir ./data
Training
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
python scripts/inference.py
By default, inference loads weight/best_model.pt and writes .npz results to result/output/.
Evaluation and Visualization
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.
- This repository retains the relevant source and attribution notices. Follow all applicable license requirements when using, modifying, or distributing its contents.