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Base_2_3/Zone/CellData/Density
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29.7k
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Base_2_3/Zone/CellData/NormalsX
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Base_2_3/Zone/CellData/NormalsY
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Base_2_3/Zone/CellData/NormalsZ
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Base_2_3/Zone/CellData/Pressure
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Base_2_3/Zone/CellData/Temperature
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Base_2_3/Zone/GridCoordinates/CoordinateX
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Base_2_3/Zone/GridCoordinates/CoordinateY
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Base_2_3/Zone/GridCoordinates/CoordinateZ
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Base_2_3/Zone/PointData/Density
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Global/Compression_ratio
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Global/Efficiency
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Global/Massflow
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Global/Omega
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End of preview. Expand in Data Studio

image/png image/png

data_production:
  physics: 3D CFD RANS compressor blade
  type: simulation
legal:
  license: CC-BY-SA
  owner: Safran
plaid:
  version: 0.1.10.dev114+gcbd3fd46f.d20251014

Example of commands:

from datasets import load_dataset
from plaid.bridges import huggingface_bridge

repo_id = "chanel/dataset"
pb_def_name = "pb_def_name" #`pb_def_name` is to choose from the repo `problem_definitions` folder

# Load the dataset
hf_datasetdict = load_dataset(repo_id)

# Load addition required data
flat_cst, key_mappings = huggingface_bridge.load_tree_struct_from_hub(repo_id)
pb_def = huggingface_bridge.load_problem_definition_from_hub(repo_id, pb_def_name)

# Efficient reconstruction of plaid samples
for split_name, hf_dataset in hf_datasetdict.items():
    for i in range(len(hf_dataset)):
        sample = huggingface_bridge.to_plaid_sample(
            hf_dataset,
            i,
            flat_cst[split_name],
            key_mappings["cgns_types"],
        )

# Extract input and output features from samples:
for t in sample.get_all_mesh_times():
    for path in pb_def.get_in_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)
    for path in pb_def.get_out_features_identifiers():
        sample.get_feature_by_path(path=path, time=t)

This dataset was generated in PLAID, we refer to this documentation for additional details on how to extract data from sample objects.

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Models trained or fine-tuned on fabiencasenave/Rotor37