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dc.contributor.authorLejeune, Emmaen_US
dc.date.accessioned2020-03-25T19:14:17Z
dc.date.available2020-03-25T19:14:17Z
dc.date.issued2020-04
dc.identifier.citationEmma Lejeune. 2020. "Mechanical MNIST: A benchmark dataset for mechanical metamodels." Extreme Mechanics Letters, Volume 36: 100659. https://doi.org/10.1016/j.eml.2020.100659
dc.identifier.issn2352-4316
dc.identifier.urihttps://hdl.handle.net/2144/39813
dc.description.abstractMetamodels, or models of models, map defined model inputs to defined model outputs. Typically, metamodels are constructed by generating a dataset through sampling a direct model and training a machine learning algorithm to predict a limited number of model outputs from varying model inputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to multi-scale simulation. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community, we introduce a benchmark data set (Mechanical MNIST) for constructing metamodels of heterogeneous material undergoing large deformation. We then show examples of how our benchmark dataset can be used, and establish baseline metamodel performance. Because our dataset is readily available, it will enable the direct quantitative comparison between different metamodeling approaches in a pragmatic manner. We anticipate that it will enable the broader community of researchers to develop improved metamodeling techniques for mechanical data that will surpass the baseline performance that we show here.en_US
dc.languageen
dc.language.isoen_US
dc.publisherElsevier BVen_US
dc.relation.ispartofExtreme Mechanics Letters
dc.subjectFinite element analysisen_US
dc.subjectMachine learningen_US
dc.subjectMechanical engineeringen_US
dc.subjectMNISTen_US
dc.subjectMetamodelen_US
dc.subjectDataseten_US
dc.titleMechanical MNIST: A benchmark dataset for mechanical metamodelsen_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1016/j.eml.2020.100659
pubs.elements-sourcecrossrefen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Mechanical Engineeringen_US
pubs.publication-statusAccepteden_US
dc.identifier.mycv553391


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