fabiencasenave/Tensile2d

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

# Load the dataset
dataset = load_dataset("fabiencasenave/Tensile2d_test", 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 = 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 contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations.

A description is provided in the MMGP paper Sections 4.1 and A.2.

The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.

Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution samples, are provided, for which outputs are not provided.

Dataset created using the PLAID library and datamodel.

• Language: PLAID
• License: cc-by-sa-4.0
• Owner: Safran

Dataset Sources

• Repository: Zenodo
• Paper: arxiv