Biegler, Lorenz
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.
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.
Keywords: computational statistics, graphical statistics, inorganic chemistry, inverse problems, data assimilation, large-scale optimization, high-performance computing, Applied Mathematics in Science, Quality & Reliability, Applied Mathematics in Science, Quality & Reliability
- Editor
- Biegler, Lorenz
- Biros, George
- Ghattas, Omar
- Heinkenschloss, Matthias
- Keyes, David
- Mallick, Bani
- Marzouk, Youssef
- Tenorio, Luis
- Waanders, Bart van Bloemen
- Willcox, Karen
- Publisher
- John Wiley and Sons, Inc.
- Publication year
- 2010
- Language
- en
- Edition
- 1
- Series
- Wiley Series in Computational Statistics
- Page amount
- 388 pages
- Category
- Natural Sciences
- Format
- Ebook
- eISBN (ePUB)
- 9781119957584
- Printed ISBN
- 9780470697436