Datasetpublic

OneScience-Group/pdenneval

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… See the full description on the dataset page: https://huggingface.co/datasets/OneScience-Group/pdenneval.

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Dataset Card

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:

text

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:

bash

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.