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 file format is PLAID, see the plaid documentation.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 sample, are provided, for which outputs are not provided.   Tips to access the data:After decompressing the downloaded file:from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinitiondataset = 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)sample = dataset[0]print("sample =", sample)for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())

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 file format is PLAID, see the plaid documentation.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 sample, are provided, for which outputs are not provided.   Tips to access the data:After decompressing the downloaded file:from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinitiondataset = 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)sample = dataset[0]print("sample =", sample)for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())

This dataset contains 2D quasistatic non-linear structural mechanics solutions, with finite elasticity and topology variations.The file format is PLAID, see the plaid documentation.The variablity in the samples are 3 input scalars and the geometry (mesh). Outputs of interest are 1 scalar and 7 fields. Sample feature variable topology, in the form of variable number of holes in the meshes.Various training and testing sets are provided (for all topologies together and for each topology), and 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 ProblemDefinitiondataset = 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)sample = dataset[0]print("sample =", sample)for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())

This dataset contains 2D quasistatic non-linear structural mechanics solutions, with finite elasticity and topology variations.The file format is PLAID, see the plaid documentation.The variablity in the samples are 3 input scalars and the geometry (mesh). Outputs of interest are 1 scalar and 7 fields. Sample feature variable topology, in the form of variable number of holes in the meshes.Various training and testing sets are provided (for all topologies together and for each topology), and 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 ProblemDefinitiondataset = 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)sample = dataset[0]print("sample =", sample)for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())

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 file format is PLAID, see the plaid documentation.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 sample, are provided, for which outputs are not provided.   Tips to access the data:After decompressing the downloaded file: 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)sample = dataset[0]print("sample =", sample)for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())