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Reduced Basis Methods for Partial Differential Equations : An Introduction

Reduced Basis Methods for Partial Differential Equations : An Introduction

Reduced Basis Methods for Partial Differential Equations : An Introduction Paperback - 2015

by Alfio Quarteroni

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Paperback. New. New Book; Fast Shipping from UK; Not signed; Not First Edition; This book provides a basic introduction to reduced basis (RB) methods for problems involving the repeated solution of partial differential equations (PDEs) arising from engineering and applied sciences, such as PDEs depending on several
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Details

  • Title Reduced Basis Methods for Partial Differential Equations : An Introduction
  • Author Alfio Quarteroni
  • Binding Paperback
  • Edition Paperback
  • Condition New
  • Pages 296
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2015-07-27
  • Bookseller's Inventory # ria9783319154305_inp
  • ISBN 9783319154305 / 3319154303
  • Weight 1.2 lbs (0.54 kg)
  • Dimensions 9.1 x 6.1 x 0.6 in (23.11 x 15.49 x 1.52 cm)
  • Category Mathematics
  • Dewey Decimal Code 003.3
  • Quantity available 356

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Reader reviews for Reduced Basis Methods for Partial Differential Equations : An Introduction

From the publisher

This book provides a basic introduction to reduced basis (RB) methods for problems involving the repeated solution of partial differential equations (PDEs) arising from engineering and applied sciences, such as PDEs depending on several parameters and PDE-constrained optimization.

The book presents a general mathematical formulation of RB methods, analyzes their fundamental theoretical properties, discusses the related algorithmic and implementation aspects, and highlights their built-in algebraic and geometric structures.

More specifically, the authors discuss alternative strategies for constructing accurate RB spaces using greedy algorithms and proper orthogonal decomposition techniques, investigate their approximation properties and analyze offline-online decomposition strategies aimed at the reduction of computational complexity. Furthermore, they carry out both a priori and a posteriori error analysis.

The whole mathematical presentation is made more stimulating by the use of representative examples of applicative interest in the context of both linear and nonlinear PDEs. Moreover, the inclusion of many pseudocodes allows the reader to easily implement the algorithms illustrated throughout the text. The book will be ideal for upper undergraduate students and, more generally, people interested in scientific computing.

All these pseudocodes are in fact implemented in a MATLAB package that is freely available at https: //github.com/redbkit

From the rear cover

This book provides a basic introduction to reduced basis (RB) methods for problems involving the repeated solution of partial differential equations (PDEs) arising from engineering and applied sciences, such as PDEs depending on several parameters and PDE-constrained optimization.

The book presents a general mathematical formulation of RB methods, analyzes their fundamental theoretical properties, discusses the related algorithmic and implementation aspects, and highlights their built-in algebraic and geometric structures.

More specifically, the authors discuss alternative strategies for constructing accurate RB spaces using greedy algorithms and proper orthogonal decomposition techniques, investigate their approximation properties and analyze offline-online decomposition strategies aimed at the reduction of computational complexity. Furthermore, they carry out both a priori and a posteriori error analysis.

The whole mathematical presentation is made more stimulating by the use of representative examples of applicative interest in the context of both linear and nonlinear PDEs. Moreover, the inclusion of many pseudocodes allows the reader to easily implement the algorithms illustrated throughout the text. The book will be ideal for upper undergraduate students and, more generally, people interested in scientific computing.

About the author

Prof. Alfio Quarteroni, Dr. Andrea Manzoni and Federico Negri - Ecole Polytechnique Fdrale de Lausanne, Lausanne, Switzerland.

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