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/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 64, in _split_generators
with h5py.File(first_file, "r") as h5:
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 564, in __init__
fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 238, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "h5py/_objects.pyx", line 56, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 57, in h5py._objects.with_phil.wrapper
File "h5py/h5f.pyx", line 102, in h5py.h5f.open
FileNotFoundError: [Errno 2] Unable to synchronously open file (unable to open file: name = 'hf://datasets/FluidVerse/SIDA@a56ab9a0a59dd96ed19759d24d38079578f98f92/train.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
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 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, 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.
Description:
This Dataset includes 128 trajectories of time-dependent Shock-Induced Droplet Aerobreakup (SIDA). Using a Finite Volume solver, we solve the two-dimensional (2D) axisymmetric compressible Euler equations for this multiphase problem.
- dataset_name: Shock-induced Droplet Aero-breakup,
- PDE: 2D axisymmetric compressible Euler equations,
- created: 08-2025,
- time_dependent: true,
- include_initial_state: true,
Spatiotemporal Information:
- num_trajectories: 128,
- num_time_steps: 61,
- num_channels: 6,
- channel_names: ["density", "pressure", "velocity_x", "velocity_y", "schlieren", "vorticity"],
- spatial_dimensions: 2,
- spatial_grid_size: [128, 256],
- dx=dy: 1.171875000e-05
Boundary conditions:
- west: Dirichlet,
- east: ZeroGradient,
- south: Axisymmetric,
- north: ZeroGradient
Two separet files, "train.h5" and "test.h5", are provided. The former includes 96 trajectories for training and validation; the latter covers the 32 remaining trajectories for inference only.
Refer to the "metadat_SIDA.json" file for more details on the dataset.
A sample Out-of-Distribution, "OOD.h5", is added as well, which covers a higher shock Mach number across 32 trajectories. For more details, refer to "metadata_SIDA_OOD.json".
Download:
The dataset can be downloaded, e.g., via huggingface-cli download.
huggingface-cli download FluidVerse/SIDA --repo-type dataset --local-dir .
Extra Data Generation:
Use the instructions inside the generation script, "generator.py", for creating larger datasets. This script runs the solver specified in the "metadata_SIDA.json" file.
Strict Licensing Notice:
This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) and is exclusively for non-commercial research and educational purposes. Any commercial use—including, but not limited to, training machine learning models, developing generative AI tools, creating software products, or other commercial R&D applications—is strictly prohibited.
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