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Large-scale inverse problems and quantification of uncertainty

Large-scale inverse problems and associated uncertainty quantification has become an important area of research, central to a wide range of science and engineering applications. Written by leading experts in the field, Large-scale Inverse Problems and Quantification of Uncertainty focuses on the com...

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Bibliographic Details
Group Author: Biegler, Lorenz T.
Published:
Literature type: Electronic eBook
Language: English
Series: Wiley series in computational statistics
Subjects:
Bayesian statistical decision theory.
Inverse problems (Differential equations)
Mathematical optimization.
Mathematics.
MATHEMATICS > Bayesian Analysis. > Bayesian Analysis.
Electronic books.
Online Access: http://onlinelibrary.wiley.com/book/10.1002/9780470685853
Summary: Large-scale inverse problems and associated uncertainty quantification has become an important area of research, central to a wide range of science and engineering applications. Written by leading experts in the field, Large-scale Inverse Problems and Quantification of Uncertainty focuses on the computational methods used to analyze and simulate inverse problems. The text provides PhD students, researchers, advanced undergraduate students, and engineering practitioners with the perspectives of researchers in areas of inverse problems and data assimilation, ranging from statistics and large-sca.
Carrier Form: 1 online resource (372 pages) : illustrations.
Bibliography: Includes bibliographical references and index.
ISBN: 9780470685860 (electronic bk.)
0470685867 (electronic bk.)
9780470685853 (electronic bk.)
0470685859 (electronic bk.)
Index Number: QA279
CLC: O212.8
Contents: Front Matter -- Introduction -- A Primer of Frequentist and Bayesian Inference in Inverse Problems / P B Stark, L Tenorio -- Subjective Knowledge or Objective Belief? An Oblique Look to Bayesian Methods / D Calvetti, E Somersalo -- Bayesian and Geostatistical Approaches to Inverse Problems / P K Kitanidis -- Using the Bayesian Framework to Combine Simulations and Physical Observations for Statistical Inference / D Higdon, K Heitmann, E Lawrence, S Habib -- Bayesian Partition Models for Subsurface Characterization / Y Efendiev, A Datta-Gupta, K Hwang, X Ma, B Mallick -- Surrogate and Reduced-Order Modeling: A Comparison of Approaches for Large-Scale Statistical Inverse Problems / M Frangos, Y Marzouk, K Willcox, B van Bloemen Waanders -- Reduced Basis Approximation and a Posteriori Error Estimation for Parametrized Parabolic PDEs: Application to Real-Time Bayesian Parameter Estimation / N C Nguyen, G Rozza, D B P Huynh, A T Patera -- Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output / J McFarland, L Swiler -- Bayesian Calibration of Expensive Multivariate Computer Experiments / R D Wilkinson -- The Ensemble Kalman Filter and Related Filters / I Myrseth, H Omre -- Using the Ensemble Kalman Filter for History Matching and Uncertainty Quantification of Complex Reservoir Models / A Seiler, G Evensen, J-A Skjervheim, J Hove, J G Vab̜ -- Optimal Experimental Design for the Large-Scale Nonlinear Ill-Posed Problem of Impedance Imaging / L Horesh, E Haber, L Tenorio -- Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach / N Zabaras -- Uncertainty Analysis for Seismic Inverse Problems: Two Practical Examples / F Delbos, C Duffet, D Sinoquet -- Solution of Inverse Problems using Discrete ODE Adjoints / A Sandu -- Index.

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