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.
Model Order Reduction. Volume 2: Snapshot-Based Methods and Algorithms / Peter, Benner; Grivet Talocia, Stefano; Alfio, Quarteroni; Gianluigi, Rozza; Wil, Schilders; Luís Miguel Silveira,. - ELETTRONICO. - 2(2021), pp. 1-356.
Titolo: | Model Order Reduction. Volume 2: Snapshot-Based Methods and Algorithms |
Autori: | |
Data di pubblicazione: | 2021 |
Abstract: | 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. |
ISBN: | 9783110671490 9783110671407 |
Appare nelle tipologie: | 7.1 Curatela |
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http://hdl.handle.net/11583/2859096
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