The dataset viewer is not available for this subset.
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/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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.
RealPDE Competition Data (NeurIPS 2026)
Training data and baseline checkpoints for the NeurIPS 2026 RealPDE Competition. This is a mirror of the competition's Google Drive release, hosted here because the Drive link runs into a per-file anonymous download quota when many people fetch it at once.
Both tracks share this release:
- Track 1, Sim2Real — codabench.org/competitions/17363
- Track 2, LTTTA — codabench.org/competitions/17385
Contents
train_sim.tar.gz simulated training trajectories, for pretraining
train_real.tar.gz real-world PIV training trajectories, for finetuning
example_data/3750_0.h5 one real trajectory, for checking shapes locally
baseline_checkpoints/
pack_ckpt_fp16.py complex-safe fp16 packing helper
sim_pretrain/ CNO, FNO, FNO-fp16, Transolver, simulation only
sim_real_ft/ the same four, finetuned on real data
The hidden evaluation set is not part of this release and is not published anywhere. It exists only inside the Codabench evaluation container.
Physical setting
Flow around a NACA4418 airfoil cross-section. Real measurements are
time-resolved Particle Image Velocimetry in a circulating tunnel; the simulated
split is matched 3D CFD under the same geometry and operating conditions.
Channels are [u, v, p], and p is zero on real data because it is not
measured. Competition evaluation runs at 32 x 64 after 2x spatial
subsampling; the raw PIV fields are 64 x 128.
example_data/3750_0.h5 stores u and v at the top level with shape
(T=868, 64, 128), plus scalars aoa and re and the grids x, y. Note the
training tarballs use a different layout, with u and v under
measured_data/.
Baseline checkpoints
All were trained with the RealPDEBench
code. Sizes: CNO 32 MB, Transolver 50 MB, FNO 403 MB in fp32 and 201 MB packed
to fp16. Hyper-parameters should be read from the state_dict shapes rather
than from the repository's yaml configs, which are not reliable for these
checkpoints; the competition starting kit documents them.
FNO spectral weights are complex64, so a naive .half() breaks them.
pack_ckpt_fp16.py stores them as view_as_real(t).half() with a
complex_keys list, and the starting kit's load_baseline.unpack_fp16 reads
that format back.
Using this in the competition
Competition rules require that training data come from this release. Model weights trained from it are permitted, including the baseline checkpoints above. Generating additional data, augmenting this release into extra training data, and finetuning a model pretrained on anything else are not permitted, and the rule is checked when the shortlisted teams are re-trained from scratch before the final ranking. The Rules page of each track is authoritative.
License
Released for non-commercial research and competition use under CC BY-NC 4.0. Do not redistribute the hidden validation or test data, which is not in this release in any case.
Contact
realpde-competition@googlegroups.com, or the forum on either Codabench page.
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