9783319377353-3319377353-Reduced Order Methods for Modeling and Computational Reduction (MS&A, 9)

Reduced Order Methods for Modeling and Computational Reduction (MS&A, 9)

ISBN-13: 9783319377353
ISBN-10: 3319377353
Edition: Softcover reprint of the original 1st ed. 2014
Author: Gianluigi Rozza, Alfio Quarteroni
Publication date: 2016
Publisher: Springer
Format: Paperback 344 pages
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Book details

ISBN-13: 9783319377353
ISBN-10: 3319377353
Edition: Softcover reprint of the original 1st ed. 2014
Author: Gianluigi Rozza, Alfio Quarteroni
Publication date: 2016
Publisher: Springer
Format: Paperback 344 pages

Summary

Reduced Order Methods for Modeling and Computational Reduction (MS&A, 9) (ISBN-13: 9783319377353 and ISBN-10: 3319377353), written by authors Gianluigi Rozza, Alfio Quarteroni, was published by Springer in 2016. With an overall rating of 4.0 stars, it's a notable title among other Computer Simulation (Computer Science, Algorithms, Programming, Mechanical, Engineering, Applied, Mathematics, Mathematical Physics, Physics, Mechanics) books. You can easily purchase or rent Reduced Order Methods for Modeling and Computational Reduction (MS&A, 9) (Paperback) from BooksRun, along with many other new and used Computer Simulation books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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.
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