PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
Paper • 2210.07182 • Published
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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-Sugon/PDENNEval model. Download the dataset and model:
hf download --dataset OneScience-Sugon/pdenneval --local-dir ./pdenneval
hf download --model OneScience-Sugon/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.
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