Datasets:

PLAID-datasets
/

Tensile2d

	---
	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
	- 505
	- 506
	- 507
	- 508
	- 509
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	- 699
	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
	- 157
	- 159
	- 168
	- 170
	- 171
	- 172
	- 174
	- 179
	- 184
	- 188
	- 195
	- 206
	- 207
	- 210
	- 226
	- 233
	- 256
	- 267
	- 279
	- 287
	- 290
	- 299
	- 302
	- 312
	- 322
	- 327
	- 343
	- 351
	- 364
	- 371
	- 395
	- 400
	- 401
	- 403
	- 405
	- 408
	- 409
	- 436
	- 446
	- 465
	- 469
	- 481
	- 496
	train_125:
	- 0
	- 4
	- 8
	- 12
	- 16
	- 17
	- 19
	- 22
	- 24
	- 33
	- 34
	- 35
	- 36
	- 37
	- 39
	- 40
	- 46
	- 49
	- 51
	- 53
	- 63
	- 64
	- 74
	- 78
	- 86
	- 89
	- 92
	- 94
	- 95
	- 99
	- 100
	- 109
	- 114
	- 138
	- 139
	- 144
	- 148
	- 151
	- 156
	- 157
	- 159
	- 163
	- 168
	- 170
	- 171
	- 172
	- 174
	- 179
	- 183
	- 184
	- 188
	- 189
	- 195
	- 201
	- 206
	- 207
	- 210
	- 212
	- 216
	- 220
	- 225
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	- 230
	- 233
	- 241
	- 255
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	- 351
	- 364
	- 371
	- 379
	- 385
	- 387
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	- 408
	- 409
	- 421
	- 422
	- 431
	- 436
	- 440
	- 444
	- 446
	- 456
	- 465
	- 466
	- 469
	- 470
	- 471
	- 481
	- 496
	train_250:
	- 0
	- 4
	- 5
	- 8
	- 9
	- 11
	- 12
	- 16
	- 17
	- 19
	- 21
	- 22
	- 24
	- 32
	- 33
	- 34
	- 35
	- 36
	- 37
	- 39
	- 40
	- 41
	- 42
	- 45
	- 46
	- 47
	- 49
	- 51
	- 53
	- 58
	- 59
	- 63
	- 64
	- 67
	- 68
	- 74
	- 76
	- 78
	- 81
	- 83
	- 86
	- 87
	- 88
	- 89
	- 90
	- 92
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	- 481
	- 491
	- 496
	train_500:
	- 0
	- 1
	- 2
	- 3
	- 4
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	- 6
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	- 9
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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, version: 0.0.10.dev0+g197feb3.d20240624.

	- 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)