Large-Scale Inverse Problems And Quantification Of Uncertainty (Wiley Series In Computational Statistics Book 713)
Published by Wiley; 1st edition (June 24, 2011), 2011
- First Edition
- Hardcover
- Used
Seller: Tacoma Book Center, Tacoma, WA, U.S.A.Tacoma Book Center
AbeBooks seller since January 10, 1998
Condition: Used - Very good
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Add to basketItem description from seller
ISBN 9780470697436. Hardback. No dustjacket. Bound in Pictorial Boards. Near Fine condition. Tight bright attractive copy with no markings to the book. As new condition. This book is Large-Scale Inverse Problems and Quantification of Uncertainty (Wiley Series in Computational Statistics Book 713).
Seller Inventory # 222839
- Title
- Large-Scale Inverse Problems And Quantification Of Uncertainty (Wiley Series In Computational Statistics Book 713)
- Author
- Lorenz Biegler (Editor), George Biros (Editor), Omar Ghattas
- Publisher
- Wiley; 1st edition (June 24, 2011)
- Publication year
- 2011
- Condition
- Very Good
- Dust jacket
- No Dustjacket
- Signed
- No Signature
- Binding
- Hardcover
- ISBN 10
- 0470697431
- ISBN 13
- 9780470697436
- Edition
- First Edition.
- Seller catalogs
- Mathematics
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.
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
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