UID:
edocfu_9959739876602883
Format:
1 online resource (VIII, 348 p.)
ISBN:
3-11-067149-2
Series Statement:
Model Order Reduction ; Volume 2
Content:
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.
Note:
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
,
In English.
Additional Edition:
ISBN 3-11-067140-9
Language:
English
DOI:
10.1515/9783110671490