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Separated Representations and PGD-Based Model Reduction: Fundamentals and Applications
By Pierre LadevÅ ze (editor) and Francisco Chinesta (editor)
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Bibliographic Detail
Publisher Springer Verlag
Publication date September 23, 2014
Pages 227
Binding Hardcover
Book category Adult Non-Fiction
ISBN-13 9783709117934
ISBN-10 3709117933
Dimensions 0.75 by 6 by 9.25 in.
Weight 1.40 lbs.
Original list price $169.00
Other format details sci/tech
Summaries and Reviews
Amazon.com description: Product 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.

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Hardcover
Book cover for 9783709117934
 
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from Springer Verlag (September 23, 2014)
9783709117934 | details & prices | 227 pages | 6.00 × 9.25 × 0.75 in. | 1.40 lbs | List price $169.00
About: 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.

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