
Separated Representations and PGD-Based Model Reduction
Fundamentals and Applications
Springer (Publisher)
Published on 23. September 2014
Book
Hardback
VII, 227 pages
978-3-7091-1793-4 (ISBN)
Description
The papers in this volume start with a description of the construction of reduced models through a review of Proper Orthogonal Decomposition (POD) and reduced basis models, including their mathematical foundations and some challenging applications, then followed by a description of a new generation of simulation strategies based on the use of separated representations (space-parameters, space-time, space-time-parameters, space-space,.), which have led to what is known as Proper Generalized Decomposition (PGD) techniques. The models can be enriched by treating parameters as additional coordinates, leading to fast and inexpensive online calculations based on richer offline parametric solutions. Separated representations are analyzed in detail in the course, from their mathematical foundations to their most spectacular applications. It is also shown how such an approximation could evolve into a new paradigm in computational science, enabling one to circumvent various computational issues in a vast array of applications in engineering science.
More details
Series
Language
English
Place of publication
Vienna
Austria
Publishing group
Springer Wien
Target group
Professional and scholarly
Research
Illustrations
40 farbige Abbildungen, 34 s/w Abbildungen
VII, 227 p. 74 illus., 40 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 19 mm
Weight
524 gr
ISBN-13
978-3-7091-1793-4 (9783709117934)
DOI
10.1007/978-3-7091-1794-1
Schweitzer Classification
Other editions
Additional editions

Francisco Chinesta | Pierre Ladevèze
Separated Representations and PGD-Based Model Reduction
Fundamentals and Applications
E-Book
09/2014
1st Edition
Springer
€96.29
Available for download
Content
From the Contents: Model order reduction based on proper orthogonal decomposition: Model reduction: extracting relevant information.- Interpolation of reduced basis: a geometrical approach.- POD for non-linear models.