Large-Scale Inverse Problems and Quantification of Uncertainty - Couverture rigide

 
9780470697436: Large-Scale Inverse Problems and Quantification of Uncertainty
Couverture rigide
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Editeur : John Wiley & Sons Inc, 2010

Synopsis

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications.

The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods.

Key Features:

  • Brings together the perspectives of researchers in areas of inverse problems and data assimilation.
  • Assesses the current state-of-the-art and identify needs and opportunities for future research.
  • Focuses on the computational methods used to analyze and simulate inverse problems.
  • Written by leading experts of inverse problems and uncertainty quantification.

Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos de l?auteur

Lorenz Biegler, Carnegie Mellon University, USA.

George Biros, Georgia Institute of Technology, USA.

Omar Ghattas, University of Texas at Austin, USA.

Matthias Heinkenschloss, Rice University, USA.

David Keyes, KAUST and Columbia University, USA.

Bani Mallick, Texas A&M University, USA.

Luis Tenorio, Colorado School of Mines, USA.

Bart van Bloemen Waanders, Sandia National Laboratories, USA.

Karen Wilcox, Massachusetts Institute of Technology, USA.

Youssef Marzouk, Massachusetts Institute of Technology, USA.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
John Wiley & Sons Inc
Date d'édition
2010
Langue
anglais
ISBN 10 
0470697431
ISBN 13 
9780470697436
Reliure
Relié
Nombre de pages
400
Éditeur
Biegler Lorenz, Biros George, Ghattas Omar, Heinkenschloss Matthias, Keyes David, Mallick Bani, Tenorio Luis, Van Bloemen Waanders Bart, Willcox Karen, Marzouk Youssef
Coordonnées du fabricant
non disponible
Personne responsable
non disponible