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OneScience-Group/
pdenneval

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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

bash

hf download OneScience-Group/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:

hf download --repo-type dataset OneScience-Group/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.