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dc.contributor.authorSchilders, Wil*
dc.contributor.authorGrivet-Talocia, Stefano*
dc.contributor.authorBenner, Peter*
dc.contributor.authorQuarteroni, Alfio*
dc.contributor.authorRozza, Gianluigi*
dc.contributor.authorSilveira, Luís Miguel*
dc.date.accessioned2021-02-12T03:46:46Z
dc.date.available2021-02-12T03:46:46Z
dc.date.issued2021*
dc.date.submitted2021-02-09 17:22:48*
dc.identifier51812*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/59498
dc.description.abstractAn increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.*
dc.languageEnglish*
dc.relation.ispartofseriesModel Order Reduction*
dc.subjectQA1-939*
dc.titleSnapshot-Based Methods and Algorithms*
dc.typebook
oapen.identifier.doi10.1515/9783110671490*
oapen.relation.isPublishedByaf2fbfcc-ee87-43d8-a035-afb9d7eef6a5*
virtual.oapen_relation_isPublishedBy.publisher_nameDe Gruyter
virtual.oapen_relation_isPublishedBy.publisher_websitehttp://www.degruyter.com/
oapen.relation.isbn9783110671490*
oapen.pages356*
oapen.volume2*


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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/