Electronic catalog

DDC 515/.357
K 56
Klibanov, (Michael V.),.
    Inverse Problems and Carleman Estimates : : Global Uniqueness, Global Convergence and Experimental Data / / Michael V. Klibanov, Jingzhi Li. - 1515/9783110745481. - Berlin ; ; Boston : : De Gruyter,, ©2021. - 1 online resource (XVI, 328 p.). ( час. мин.), 1515/9783110745481. - (Inverse and Ill-Posed Problems Series ; ; volume 63). - In English. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/1F9617F7-6E58-4E26-A925-7E3B4DD5906F. - ISBN 3110745488 (electronic book). - ISBN 9783110745481 (electronic bk.)
Description based on online resource; title from digital title page (viewed on October 06, 2021).
Параллельные издания:
1. Print version: :
2. Print version: :

Klibanov, (Michael V.),. Inverse Problems and Carleman Estimates : [Электронный ресурс] : Global Uniqueness, Global Convergence and Experimental Data / / Michael V. Klibanov, Jingzhi Li., ©2021. - 1 online resource (XVI, 328 p.). с.

DDC 515.357
L 31

    Large scale inverse problems : : computational methods and applications in the earth sciences / / edited by Mike Cullen, Melina A. Freitag, Stefan Kindermann, Robert Scheichl. - Berlin ; ; Boston : : De Gruyter,, ©2013. - 1 online resource (ix, 203 pages) : : il. - (Radon Series on Computational and Applied Mathematics). - English. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/FD27C70C-43D8-4817-B780-1EA6D58FA8AF. - ISBN 9783110282269 (electronic bk.). - ISBN 3110282267 (electronic bk.). - ISBN 3110282224. - ISBN 9783110282221
Print version record.
Параллельные издания: Print version: : Scheichl, Robert. Large Scale Inverse Problems : Computational Methods and Applications in the Earth Sciences. - Berlin : De Gruyter, ©2013. - ISBN 9783110282221
    Содержание:
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 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.
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.
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.
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.

Large scale inverse problems : [Электронный ресурс] : computational methods and applications in the earth sciences / / edited by Mike Cullen, Melina A. Freitag, Stefan Kindermann, Robert Scheichl., ©2013. - 1 online resource (ix, 203 pages) : с. (Введено оглавление)