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bcTiann
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diffphycon-jellyfish-dataset

Tasks:
Reinforcement Learning
Unconditional Image Generation
License:
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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/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.

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DiffPhyCon 2D Jellyfish Dataset

This repository is an archival mirror of the official 2D Jellyfish dataset released by AI4Science-WestlakeU for the NeurIPS 2024 paper “DiffPhyCon: A Generative Approach to Control Complex Physical Systems.”

The dataset was not generated or authored by bcTiann. It is mirrored here to support reproducible DiffPhyCon baseline and Flow Matching experiments. The data values have not been modified; only lossless tar sharding was applied to make storage and transfer through the Hugging Face Hub reliable.

Original source

Please cite the original DiffPhyCon paper and credit its authors when using this dataset.

Contents

  • train_data: 30,000 simulations.
  • test_data: 200 simulations.
  • Per-simulation fluid states, forces, boundaries, opening-angle controls, and merged boundary representations.
  • Training and test normalization metadata.

To avoid Hub commit-rate limits from roughly 150,000 source files, the files are stored in lossless tar shards:

  • shards/train_states_*.tar: the 30,000 training-state files, split into 2,000 simulations per shard.
  • shards/train_auxiliary.tar: training forces, boundaries, controls, merged masks, and normalization metadata.
  • shards/test_data.tar: the complete 200-simulation test split.
  • SHA256SUMS: archive integrity checksums.

Extracting the archives recreates the original train_data and test_data directory layout. The main source subdirectories are states, forces, bdry128, bdry64, bdry_head_thetas, and bdry_merged_mask_offsets.

License and redistribution status

The official DiffPhyCon source-code repository is distributed under the MIT License. However, the Jellyfish dataset and checkpoints are distributed externally through Google Drive, and no separate dataset license was found in the official repository README or in the downloaded dataset package.

For that reason, this mirror does not assert that the dataset itself is MIT-licensed, and the Hugging Face metadata uses license: other. This public archival mirror preserves explicit attribution to the original DiffPhyCon authors; users should consult the original authors before further redistribution or commercial use.

Citation 

@inproceedings{wei2024diffphycon,
  title     = {DiffPhyCon: A Generative Approach to Control Complex Physical Systems},
  author    = {Wei, Long and Hu, Peiyan and Feng, Ruiqi and Feng, Haodong and
               Du, Yixuan and Zhang, Tao and Wang, Rui and Wang, Yue and
               Ma, Zhi-Ming and Wu, Tailin},
  booktitle = {The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year      = {2024},
  url       = {https://openreview.net/forum?id=MbZuh8L0Xg}
}
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