Large-Scale Inverse Problems and Quantification of Uncertainty
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.
388 pages; ISBN 9781119957584
, or download in or
Title: Large-Scale Inverse Problems and Quantification of Uncertainty
Author: Lorenz Biegler; George Biros; Omar Ghattas; Matthias Heinkenschloss; David Keyes; Bani Mallick ; Luis Tenorio; Bart van Bloemen Waanders; Karen Willcox; Youssef Marzouk
A Gentle Introduction to Optimization 2014 US$ 32.00 284 pages
Alan M. Turing 2012 US$ 13.00 210 pages