Large-Scale Inverse Problems and Quantification of Uncertainty
Copyright © 2011 John Wiley & Sons, Ltd
Editor(s): Lorenz Biegler, George Biros, Omar Ghattas, Matthias Heinkenschloss, David Keyes, Bani Mallick, Youssef Marzouk, Luis Tenorio, Bart van Bloemen Waanders, Karen Willcox
Published Online: 12 OCT 2010 10:37PM EST
Print ISBN: 9780470697436
Online ISBN: 9780470685853
About this Book
About The Product
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.
• 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.