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dc.contributor.editorBenner, Peter
dc.contributor.editorSchilders, Wil
dc.contributor.editorGrivet-Talocia, Stefano
dc.contributor.editorQuarteroni, Alfio
dc.contributor.editorRozza, Gianluigi
dc.contributor.editorMiguel Silveira, Luís
dc.date.accessioned2025-03-08T05:10:36Z
dc.date.available2025-03-08T05:10:36Z
dc.date.issued2020
dc.date.submitted2021-02-11T17:55:54Z
dc.identifierONIX_20210211_9783110671490_65
dc.identifierhttps://library.oapen.org/handle/20.500.12657/46696
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/181145
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.rightsopen access
dc.subject.otherApplied mathematics
dc.subject.otherPROM
dc.subject.otherthema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis
dc.subject.otherthema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics
dc.subject.otherthema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics
dc.subject.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering
dc.titleModel Order Reduction
dc.title.alternativeVolume 2: Snapshot-Based Methods and Algorithms
dc.typebook
oapen.identifier.doi10.1515/9783110671490
oapen.relation.isPublishedByaf2fbfcc-ee87-43d8-a035-afb9d7eef6a5
oapen.pages348
oapen.place.publicationBerlin/Boston
dc.seriesnumberVolume 2


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