---
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:
  description:
    legal:
      owner: Safran
      license: cc-by-sa-4.0
    data_production:
      type: simulation
      physics: 2D quasistatic non-linear structural mechanics, small deformations,
        plane strain
    split:
      test:
      - 500
      - 501
      - 502
      - 503
      - 504
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      OOD:
      - 700
      - 701
      train_8:
      - 35
      - 95
      - 188
      - 210
      - 312
      - 322
      - 401
      - 408
      train_16:
      - 17
      - 35
      - 64
      - 95
      - 170
      - 174
      - 184
      - 188
      - 210
      - 267
      - 290
      - 312
      - 322
      - 401
      - 408
      - 496
      train_32:
      - 12
      - 17
      - 19
      - 35
      - 64
      - 92
      - 95
      - 99
      - 144
      - 148
      - 159
      - 170
      - 171
      - 174
      - 184
      - 188
      - 206
      - 210
      - 267
      - 290
      - 312
      - 322
      - 364
      - 371
      - 395
      - 400
      - 401
      - 403
      - 408
      - 436
      - 481
      - 496
      train_64:
      - 4
      - 12
      - 17
      - 19
      - 22
      - 24
      - 35
      - 40
      - 53
      - 64
      - 78
      - 86
      - 92
      - 95
      - 99
      - 109
      - 114
      - 138
      - 144
      - 148
      - 156
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      - 168
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      - 343
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      - 395
      - 400
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      - 403
      - 405
      - 408
      - 409
      - 436
      - 446
      - 465
      - 469
      - 481
      - 496
      train_125:
      - 0
      - 4
      - 8
      - 12
      - 16
      - 17
      - 19
      - 22
      - 24
      - 33
      - 34
      - 35
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      - 456
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      - 481
      - 496
      train_250:
      - 0
      - 4
      - 5
      - 8
      - 9
      - 11
      - 12
      - 16
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      - 67
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      train_500:
      - 0
      - 1
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    task: regression
    in_scalars_names:
    - P
    - p1
    - p2
    - p3
    - p4
    - p5
    out_scalars_names:
    - max_von_mises
    - max_q
    - max_U2_top
    - max_sig22_top
    in_timeseries_names: []
    out_timeseries_names: []
    in_fields_names: []
    out_fields_names:
    - U1
    - U2
    - q
    - sig11
    - sig22
    - sig12
    in_meshes_names:
    - /Base_2_2/Zone
    out_meshes_names: []
  features:
  - name: sample
    dtype: binary
  splits:
  - name: all_samples
    num_bytes: 3571624
    num_examples: 4
  download_size: 1705231
  dataset_size: 3571624
---

# Dataset Card
![image/png](https://i.ibb.co/Js062hF/preview.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](https://plaid-lib.readthedocs.io/en/latest/autoapi/plaid/containers/sample/index.html#plaid.containers.sample.Sample).
Mesh objects included in samples follow the [CGNS](https://cgns.github.io/) standard, and can be converted in
[Muscat.Containers.Mesh.Mesh](https://muscat.readthedocs.io/en/latest/_source/Muscat.Containers.Mesh.html#Muscat.Containers.Mesh.Mesh).


Example of commands:
```python
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 ](https://arxiv.org/pdf/2305.12871) 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](https://plaid-lib.readthedocs.io/) library and datamodel.
    
- **Language:** [PLAID](https://plaid-lib.readthedocs.io/)
- **License:** cc-by-sa-4.0
- **Owner:** Safran

### Dataset Sources

- **Repository:** [Zenodo](https://zenodo.org/records/10124594)
- **Paper:** [arxiv](https://arxiv.org/pdf/2305.12871)