Model Order Reduction. / Volume 2, : Snapshot-Based Methods and Algorithms / / Peter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luís Miguel Silveira.

• MitwirkendeR: Alfio, Quarteroni,; Andrea, Manzoni,; Andreas, Buhr,; Anthony T., Patera,; Carmen, Gräßle,; Charbel, Farhat,; Francesco, Ballarin,; Francisco, Chinesta,; Gianluigi, Rozza,; Giovanni, Stabile,; J. Nathan, Kutz,; Kathrin, Smetana,; Laura, Iapichino,; Marco, Tezzele,; Martin, Hess,; Hinze, Michael,; Pierre, Ladevèze,; Sebastian, Grimberg,; Stefan, Volkwein,; Steven L., Brunton,; Yvon, Maday,
• HerausgeberIn: Benner, Peter,; Grivet-Talocia, Stefano,; Quarteroni, Alfio,; Rozza, Gianluigi,; Schilders, W. H. A.; Silveira, Luís Miguel,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter,, [2020]; ©2021
• Year of Publication: 2020
• Language: English
• Series: Model Order Reduction ; Volume 2
• Physical Description: 1 online resource (VIII, 348 p.)

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.

Table of Contents

Frontmatter -- Preface to the second volume of Model Order Reduction -- Contents -- 1 Basic ideas and tools for projection-based model reduction of parametric partial differential equations -- 2 Model order reduction by proper orthogonal decomposition -- 3 Proper generalized decomposition -- 4 Reduced basis methods -- 5 Computational bottlenecks for PROMs: precomputation and hyperreduction -- 6 Localized model reduction for parameterized problems -- 7 Data-driven methods for reduced-order modeling -- Index