Datasets:
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:
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
- 510
- 511
- 512
- 513
- 514
- 515
- 516
- 517
- 518
- 519
- 520
- 521
- 522
- 523
- 524
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- 526
- 527
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- 641
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- 650
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- 689
- 690
- 691
- 692
- 693
- 694
- 695
- 696
- 697
- 698
- 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
- 226
- 228
- 230
- 233
- 241
- 255
- 256
- 262
- 267
- 268
- 275
- 277
- 279
- 287
- 289
- 290
- 296
- 299
- 300
- 301
- 302
- 311
- 312
- 314
- 318
- 322
- 327
- 329
- 341
- 343
- 347
- 348
- 351
- 364
- 371
- 379
- 385
- 387
- 390
- 392
- 394
- 395
- 400
- 401
- 403
- 405
- 407
- 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
- 94
- 95
- 96
- 99
- 100
- 101
- 103
- 105
- 106
- 109
- 110
- 111
- 112
- 114
- 116
- 122
- 125
- 126
- 127
- 128
- 130
- 131
- 136
- 137
- 138
- 139
- 144
- 146
- 147
- 148
- 151
- 152
- 156
- 157
- 159
- 162
- 163
- 166
- 168
- 170
- 171
- 172
- 173
- 174
- 179
- 180
- 183
- 184
- 188
- 189
- 195
- 199
- 201
- 204
- 205
- 206
- 207
- 208
- 210
- 211
- 212
- 213
- 214
- 216
- 218
- 220
- 222
- 223
- 225
- 226
- 227
- 228
- 230
- 231
- 233
- 236
- 241
- 242
- 245
- 248
- 251
- 252
- 255
- 256
- 257
- 258
- 259
- 261
- 262
- 264
- 267
- 268
- 272
- 275
- 277
- 279
- 280
- 282
- 283
- 285
- 286
- 287
- 289
- 290
- 291
- 294
- 295
- 296
- 299
- 300
- 301
- 302
- 307
- 309
- 311
- 312
- 313
- 314
- 315
- 316
- 318
- 322
- 324
- 327
- 329
- 331
- 332
- 340
- 341
- 343
- 344
- 347
- 348
- 350
- 351
- 355
- 358
- 364
- 366
- 367
- 371
- 372
- 374
- 375
- 376
- 377
- 379
- 385
- 387
- 388
- 390
- 392
- 394
- 395
- 396
- 397
- 399
- 400
- 401
- 402
- 403
- 404
- 405
- 407
- 408
- 409
- 411
- 412
- 413
- 418
- 419
- 421
- 422
- 424
- 426
- 431
- 436
- 438
- 439
- 440
- 442
- 444
- 445
- 446
- 448
- 451
- 455
- 456
- 457
- 458
- 465
- 466
- 468
- 469
- 470
- 471
- 473
- 474
- 475
- 476
- 477
- 481
- 491
- 496
train_500:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
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- 86
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- 95
- 96
- 97
- 98
- 99
- 100
- 101
- 102
- 103
- 104
- 105
- 106
- 107
- 108
- 109
- 110
- 111
- 112
- 113
- 114
- 115
- 116
- 117
- 118
- 119
- 120
- 121
- 122
- 123
- 124
- 125
- 126
- 127
- 128
- 129
- 130
- 131
- 132
- 133
- 134
- 135
- 136
- 137
- 138
- 139
- 140
- 141
- 142
- 143
- 144
- 145
- 146
- 147
- 148
- 149
- 150
- 151
- 152
- 153
- 154
- 155
- 156
- 157
- 158
- 159
- 160
- 161
- 162
- 163
- 164
- 165
- 166
- 167
- 168
- 169
- 170
- 171
- 172
- 173
- 174
- 175
- 176
- 177
- 178
- 179
- 180
- 181
- 182
- 183
- 184
- 185
- 186
- 187
- 188
- 189
- 190
- 191
- 192
- 193
- 194
- 195
- 196
- 197
- 198
- 199
- 200
- 201
- 202
- 203
- 204
- 205
- 206
- 207
- 208
- 209
- 210
- 211
- 212
- 213
- 214
- 215
- 216
- 217
- 218
- 219
- 220
- 221
- 222
- 223
- 224
- 225
- 226
- 227
- 228
- 229
- 230
- 231
- 232
- 233
- 234
- 235
- 236
- 237
- 238
- 239
- 240
- 241
- 242
- 243
- 244
- 245
- 246
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- 248
- 249
- 250
- 251
- 252
- 253
- 254
- 255
- 256
- 257
- 258
- 259
- 260
- 261
- 262
- 263
- 264
- 265
- 266
- 267
- 268
- 269
- 270
- 271
- 272
- 273
- 274
- 275
- 276
- 277
- 278
- 279
- 280
- 281
- 282
- 283
- 284
- 285
- 286
- 287
- 288
- 289
- 290
- 291
- 292
- 293
- 294
- 295
- 296
- 297
- 298
- 299
- 300
- 301
- 302
- 303
- 304
- 305
- 306
- 307
- 308
- 309
- 310
- 311
- 312
- 313
- 314
- 315
- 316
- 317
- 318
- 319
- 320
- 321
- 322
- 323
- 324
- 325
- 326
- 327
- 328
- 329
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- 340
- 341
- 342
- 343
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- 345
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- 348
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- 350
- 351
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- 363
- 364
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- 431
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- 450
- 451
- 452
- 453
- 454
- 455
- 456
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- 458
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- 496
- 497
- 498
- 499
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: 864827523
num_examples: 702
download_size: 395394264
dataset_size: 864827523
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 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