Format:
Online-Ressource (1 online resource (216 pages))
,
illustrations.
Edition:
Online-Ausg.
ISBN:
9783110282269
,
9783110282221
Series Statement:
Radon series on computational and applied mathematics 1865-3707 volume 13
Content:
This book is thesecond volume of three volume series recording the "Radon Special Semester 2011 on Multiscale Simulation & Analysis in Energy and the Environment" taking place in Linz, Austria, October 3-7, 2011. The volume addresses the common ground in the mathematical and computational procedures required for large-scale inverse problems and data assimilation in forefront applications.
Content:
Intro -- Preface -- Synergy of inverse problems and data assimilation techniques -- 1 Introduction -- 2 Regularization theory -- 3 Cycling, Tikhonov regularization and 3DVar -- 4 Error analysis -- 5 Bayesian approach to inverse problems -- 6 4DVar -- 7 Kalman filter and Kalman smoother -- 8 Ensemble methods -- 9 Numerical examples -- 9.1 Data assimilation for an advection-diffusion system -- 9.2 Data assimilation for the Lorenz-95 system -- 10 Concluding remarks -- Variational data assimilation for very large environmental problems -- 1 Introduction -- 2 Theory of variational data assimilation -- 2.1 Incremental variational data assimilation -- 3 Practical implementation -- 3.1 Model development -- 3.2 Background error covariances -- 3.3 Observation errors -- 3.4 Optimization methods -- 3.5 Reduced order approaches -- 3.6 Issues for nested models -- 3.7 Weak-constraint variational assimilation -- 4 Summary and future perspectives -- Ensemble filter techniques for intermittent data assimilation -- 1 Bayesian statistics -- 1.1 Preliminaries -- 1.2 Bayesian inference -- 1.3 Coupling of random variables -- 1.4 Monte Carlo methods -- 2 Stochastic processes -- 2.1 Discrete time Markov processes -- 2.2 Stochastic difference and differential equations -- 2.3 Ensemble prediction and sampling methods -- 3 Data assimilation and filtering -- 3.1 Preliminaries -- 3.2 SequentialMonte Carlo method -- 3.3 Ensemble Kalman filter (EnKF) -- 3.4 Ensemble transform Kalman-Bucy filter -- 3.5 Guided sequential Monte Carlo methods -- 3.6 Continuous ensemble transform filter formulations -- 4 Concluding remarks -- Inverse problems in imaging -- 1 Mathematicalmodels for images -- 2 Examples of imaging devices -- 2.1 Optical imaging -- 2.2 Transmission tomography -- 2.3 Emission tomography -- 2.4 MR imaging -- 2.5 Acoustic imaging -- 2.6 Electromagnetic imaging.
Note:
Includes bibliographical references. - Description based on online resource; title from PDF title page (ebrary, viewed November 6, 2013)
Additional Edition:
ISBN 9783110282221
Additional Edition:
Erscheint auch als Druck-Ausgabe ISBN 9783110282221
Language:
English
Keywords:
Electronic books
URL:
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