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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2023
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| Online Access: | https://doi.org/10.5281/zenodo.10124594 |
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| _version_ | 1866902208132415488 |
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| author | Casenave, Fabien Roynard, Xavier Staber, Brian |
| author_facet | Casenave, Fabien Roynard, Xavier Staber, Brian |
| contents | <p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>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. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p> </p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_10124594 |
| institution | Zenodo |
| language | eng |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations Casenave, Fabien Roynard, Xavier Staber, Brian AI Machine Learning Physics Geometrical variations <p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>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. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p> </p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p> |
| title | Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations |
| topic | AI Machine Learning Physics Geometrical variations |
| url | https://doi.org/10.5281/zenodo.10124594 |