license: other
tags:
- Operator Learning
- Neural PDE Solvers
language:
- en
- zh
PDENNEval
Dataset Description
PDENNEval is a comprehensive dataset for evaluating neural-network-based PDE solving methods, introduced in an IJCAI 2024 paper. It covers function learning and operator learning tasks and includes 15 types of PDE problems across multiple scientific domains, including fluids, materials, finance, and electromagnetics.
The dataset consists of 10 PDEBench data files and 6 self-generated data files, totaling approximately 286.9 GB. It can be used for model sanity checks, training, evaluation, and comparisons across problems.
Paper: PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs
Supported Tasks
| Scenario | Description |
|---|---|
| PDE solution field prediction | Predict PDE solutions from equation parameters, coordinates, or initial conditions. |
| Function learning evaluation | Compare neural networks' ability to solve individual PDE problems. |
| Operator learning evaluation | Evaluate model generalization across different input conditions and equation tasks. |
| Cross-domain scientific computing | Cover fluid, diffusion-reaction, materials, finance, and electromagnetics problems. |
Dataset Format and Structure
All data files are stored in HDF5 format with the .hdf5 or .h5 extension:
data/
PDEBench data files
Self-generated PDE data files
The data includes coordinates, time, coefficient fields, boundary conditions, and solution fields for one-, two-, and three-dimensional PDEs. It covers problems such as Advection, Burgers, Diffusion-Reaction, Darcy Flow, Shallow Water, Allen-Cahn, Cahn-Hilliard, Navier-Stokes, Euler, Maxwell, and Black-Scholes-Barenblatt.
Fields and tensor shapes vary across PDEs. Refer to the data files and metadata/pdenneval_schema.json for the specific schema.
How to Use the Dataset
This dataset is designed for the OneScience-Group/PDENNEval model. Download the dataset and model:
hf download --dataset OneScience-Group/pdenneval --local-dir ./pdenneval
hf download --model OneScience-Group/PDENNEval --local-dir ./PDENNEval
Official OneScience Information
| Platform | OneScience 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 |
Citation and License
- Original PDENNEval paper: PDENNEval: A Comprehensive Evaluation of Neural Network Methods for Solving PDEs
- Original PDEBench paper: PDEBench: An Extensive Benchmark for Scientific Machine Learning
- PDEBench dataset: PDEBench Datasets
- This repository retains source attribution. Before public distribution or republication, confirm the licensing requirements with the upstream projects.