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2D_ElastoPlastoDynamics: 2D dynamic non-linear structural mechanics dataset, with a non-linear non-local constitutive law

Authors: PIAT, William; Casenave, Fabien;

2D_ElastoPlastoDynamics: 2D dynamic non-linear structural mechanics dataset, with a non-linear non-local constitutive law

Abstract

This dataset contains 2D dynamic non-linear structural mechanics with a non-linear non-local constitutive law. The files format is PLAID, see the plaid documentation. The variablity in the samples is the geometry (mesh). Outputs of interest are 3 transient fields: x and y components of the displacement at the nodes and the erosion status at the elements. The dataset has a training set of size 1000 and a testing set of size 18. Outputs are not provided on the testing sets. Tips to access the data: After decompressing the downloaded file: from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition dataset = Dataset()problem = ProblemDefinition() problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True) print("problem =", problem)print("dataset =", dataset) ids_train = problem.get_split('train') ids_test = problem.get_split('test') sample_train_0 = dataset[ids_train[0]] sample_test_0 = dataset[ids_test[0]] print(sample_train_0) print(sample_test_0) sample = sample_train_0 # inputs mesh = sample.get_mesh() mesh = sample.get_mesh(time=0.01, apply_links=True, in_memory=True)# links to mesh at time=0 since mesh is constant print(mesh) # outputs for fn in ["U_x", "U_y"]: field = sample.get_field(fn, time=0.01) print(field) field = sample.get_field("EROSION_STATUS", location="CellCenter", time=0.01) print(field)

Keywords

AI, Physics, Machine learning, Geometrical variations, Simulation, Structural mechanics

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average