Model Order Reduction
Volume 2: Snapshot-Based Methods and Algorithms
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https://library.oapen.org/bitstream/20.500.12657/46696/1/9783110671490.pdf
Contributor(s)
Benner, Peter (editor)
Schilders, Wil (editor)
Grivet-Talocia, Stefano (editor)
Quarteroni, Alfio (editor)
Rozza, Gianluigi (editor)
Miguel Silveira, Luís (editor)
Language
EnglishAbstract
An 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.
Keywords
Applied mathematics; PROM; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBK Calculus and mathematical analysis::PBKS Numerical analysis; thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics; thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineeringPublisher
De GruyterPublisher website
http://www.degruyter.com/Publication date and place
Berlin/Boston, 2020Series
Model Order Reduction,Pages
348
Except where otherwise noted, this item's license is described
as
open access