PLAID-datasets/AirfRANS_remeshed

Dataset Card

This dataset contains a single huggingface split, named 'all_samples'.

The samples contains a single huggingface feature, named called "sample".

Samples are instances of plaid.containers.sample.Sample. Mesh objects included in samples follow the CGNS standard, and can be converted in Muscat.Containers.Mesh.Mesh.

Example of commands:

import pickle
from datasets import load_dataset
from plaid.containers.sample import Sample

# Load the dataset
dataset = load_dataset("chanel/dataset", split="all_samples")

# Get the first sample of the first split
split_names = list(dataset.description["split"].keys())
ids_split_0 = dataset.description["split"][split_names[0]]
sample_0_split_0 = dataset[ids_split_0[0]]["sample"]
plaid_sample = Sample.model_validate(pickle.loads(sample_0_split_0))
print("type(plaid_sample) =", type(plaid_sample))

print("plaid_sample =", plaid_sample)

# Get a field from the sample
field_names = plaid_sample.get_field_names()
field = plaid_sample.get_field(field_names[0])
print("field_names[0] =", field_names[0])

print("field.shape =", field.shape)

# Get the mesh and convert it to Muscat
from Muscat.Bridges import CGNSBridge
CGNS_tree = plaid_sample.get_mesh()
mesh = CGNSBridge.CGNSToMesh(CGNS_tree)
print(mesh)

Dataset Details

Dataset Description

This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 4 main differences:

• it is in PLAID format
• all meshes were cliped in a box [2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview
• all meshes were remeshed using MMG to reduce the number of nodes and cells
• a ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge

It has 2 variants:

• AirfRANS_clipped
• AirfRANS_original

Dataset created using the PLAID library and datamodel, version: 0.0.10.dev0+g197feb3.d20240624.

• Language: PLAID
• License: odbl
• Owner: Safran

Dataset Sources

• Repository: Zenodo
• Paper: arxiv