Large-scale inverse problems and quantification of uncertainty / / edited by Lorenz Biegler ... [et al.]

• Pubblicazione: Chichester, West Sussex, : Wiley, 2011
• Descrizione fisica: 1 online resource (390 p.)
• Disciplina: 515/.357
• Soggetto topico: Bayesian statistical decision theory; Inverse problems (Differential equations); Mathematical optimization
• Altri autori: BieglerLorenz T
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Large-Scale Inverse Problems and Quantification of Uncertainty; Index; Contents; List of Contributors; 1 Introduction; 2 A Primer of Frequentist and Bayesian Inference in Inverse Problems; 3 Subjective Knowledge or Objective Belief? An Oblique Look to Bayesian Methods; 4 Bayesian and Geostatistical Approaches to Inverse Problems; 5 Using the Bayesian Framework to Combine Simulations and Physical Observations for Statistical Inference; 6 Bayesian Partition Models for Subsurface Characterization ; 7 Surrogate and Reduced-Order Modeling: A Comparison of Approaches for Large-Scale Statistical Inverse Problems8 Reduced Basis Approximation and A Posteriori Error Estimation for Parametrized Parabolic PDEs: Application to Real-Time Bayesian Parameter Estimation; 9 Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output; 10 Bayesian Calibration of Expensive Multivariate Computer Experiments; 11 The Ensemble Kalman Filter and Related Filters; 12 Using the Ensemble Kalman Filter for History Matching and Uncertainty Quantification of Complex Reservoir Models ; 13 Optimal Experimental Design for the Large-Scale Nonlinear Ill-Posed Problem of Impedance Imaging14 Solving Stochastic Inverse Problems: A Sparse Grid Collocation Approach; 15 Uncertainty Analysis for Seismic Inverse Problems: Two Practical Examples; 16 Solution of Inverse Problems Using Discrete ODE Adjoints
• Sommario/riassunto: 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
• Titolo autorizzato: Large-scale inverse problems and quantification of uncertainty
• ISBN: 9786612848971; 9781119957584; 1119957583; 9781282848979; 1282848976; 9780470685853; 0470685859; 9780470685860; 0470685867
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910827728103321
• Lo trovi qui: Univ. Federico II
• Serie: Wiley series in computational statistics.