Model Order Reduction. Volume 2, Snapshot-Based Methods and Algorithms / Peter Benner, Wil Schilders, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Luâis Miguel Silveira.
Material type: TextPublisher: Berlin ; Boston : De Gruyter, [2020]Copyright date: ©2021Description: 1 online resource (VIII, 348 pages)Content type:- text
- computer
- online resource
- 3110671492
- 9783110671490
- 511.8 23
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
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.
In English.
Description based on online resource; title from PDF title page (publisher's Web site, viewed 06. Jan 2021).
Master record variable field(s) change: 050, 082, 650
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