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|a Large scale inverse problems :
|b computational methods and applications in the earth sciences /
|c edited by Mike Cullen, Melina A. Freitag, Stefan Kindermann, Robert Scheichl.
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|a Berlin ;
|a Boston :
|b De Gruyter,
|c [2013]
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|c ©2013
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|a 1 online resource (ix, 203 pages) :
|b illustrations
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|a text
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|b c
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|a online resource
|b cr
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|a Radon Series on Computational and Applied Mathematics
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|a Includes bibliographical references.
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|a 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.
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|a 2.1 Incremental variational data assimilation3 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.
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|a 2.2 Stochastic difference and differential equations2.3 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.
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|a 2.6 Electromagnetic imaging3 Basic image reconstruction; 3.1 Deblurring and point spread functions; 3.2 Noise; 3.3 Reconstruction methods; 4 Missing data and prior information; 4.1 Prior information; 4.2 Undersampling and superresolution; 4.3 Inpainting; 4.4 Surface imaging; 5 Calibration problems; 5.1 Blind deconvolution; 5.2 Nonlinear MR imaging; 5.3 Attenuation correction in SPECT; 5.4 Blind spectral unmixing; 6 Model-based dynamic imaging; 6.1 Kinetic models; 6.2 Parameter identification; 6.3 Basis pursuit; 6.4 Motion and deformation models; 6.5 Advanced PDE models.
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|a The lost honor of l2-based regularization1 Introduction; 2 l1-based regularization; 3 Poor data; 4 Large, highly ill-conditioned problems; 4.1 Inverse potential problem; 4.2 The effect of ill-conditioning on L1 regularization; 4.3 Nonlinear, highly ill-posed examples; 5 Summary; List of contributors.
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|a 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.
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|a Print version record.
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|f This work is licensed under a Creative Commons license
|u https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
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|a English.
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|a Open Access
|5 EbpS
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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|a De Gruyter Online
|b De Gruyter Open Access eBooks
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|a Inverse problems (Differential equations)
|
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|a Problèmes inverses (Équations différentielles)
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|a Applied mathematics.
|2 bicssc
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|a MATHEMATICS
|x Calculus.
|2 bisacsh
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|a MATHEMATICS
|x Mathematical Analysis.
|2 bisacsh
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|a Inverse problems (Differential equations)
|2 fast
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|a Data Assimilation.
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|a Geosciences.
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|a Ill-Posed Inverse Problems.
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|a Optimization.
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|a Regularization.
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1 |
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|a Cullen, Michael J. P.,
|e editor.
|
700 |
1 |
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|a Freitag, Melina A.,
|d 1980-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PBJbRjWgYw8MpXDwWhh9FKd
|
700 |
1 |
|
|a Kindermann, Stefan,
|d 1972-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PBJpDRPfyq9b4mtfmfkdCwC
|
700 |
1 |
|
|a Scheichl, Robert,
|d 1972-
|e editor.
|1 https://id.oclc.org/worldcat/entity/E39PCjwRR9mJDtdQ6bYM3WxGQC
|
758 |
|
|
|i has work:
|a Large scale inverse problems (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGFRx4KBfKXYbdyjMMyBGd
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
776 |
0 |
8 |
|i Print version:
|a Scheichl, Robert.
|t Large Scale Inverse Problems : Computational Methods and Applications in the Earth Sciences.
|d Berlin : De Gruyter, ©2013
|z 9783110282221
|
830 |
|
0 |
|a Radon series on computational and applied mathematics.
|
856 |
4 |
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|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=1113333
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