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Tensile2d

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Graph Machine Learning
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Tags:
physics learning
geometry learning
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Tensile2d / README.md
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metadata
license: cc-by-sa-4.0
size_categories:
  - n<1K
task_categories:
  - graph-ml
pretty_name: 2D quasistatic non-linear structural mechanics solutions
tags:
  - physics learning
  - geometry learning
configs:
  - config_name: default
    data_files:
      - split: all_samples
        path: data/all_samples-*
dataset_info:
  features:
    - name: sample
      dtype: binary
  splits:
    - name: all_samples
      num_bytes: 864827523
      num_examples: 702
  download_size: 395394264
  dataset_size: 864827523

Dataset Card

image/png

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 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, version: 0.0.10.dev0+g197feb3.d20240624.

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

Dataset Sources