Reduced Order Methods for Modeling and Computational Reduction edited by Alfio Quarteroni, Gianluigi Rozza.

This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mec...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Quarteroni, Alfio (Editor), Rozza, Gianluigi (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2014.
Edition:1st ed. 2014.
Series:MS&A, Modeling, Simulation and Applications, 9
Springer eBook Collection.
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Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Description
Summary:This monograph addresses the state of the art of reduced order methods for modeling and computational reduction of complex parametrized systems, governed by ordinary and/or partial differential equations, with a special emphasis on real time computing techniques and applications in computational mechanics, bioengineering and computer graphics.  Several topics are covered, including: design, optimization, and control theory in real-time with applications in engineering; data assimilation, geometry registration, and parameter estimation with special attention to real-time computing in biomedical engineering and computational physics; real-time visualization of physics-based simulations in computer science; the treatment of high-dimensional problems in state space, physical space, or parameter space; the interactions between different model reduction and dimensionality reduction approaches; the development of general error estimation frameworks which take into account both model and discretization effects. This book is primarily addressed to computational scientists interested in computational reduction techniques for large scale differential problems.
Physical Description:X, 334 p. online resource.
ISBN:9783319020907
ISSN:2037-5255 ;
DOI:10.1007/978-3-319-02090-7