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New Trends in Parameter Identification for Mathematical Models

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Cover of 'New Trends in Parameter Identification for Mathematical Models'

Table of Contents

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    Book Overview
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    Chapter 1 Posterior Contraction in Bayesian Inverse Problems Under Gaussian Priors
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    Chapter 2 Convex Regularization of Discrete-Valued Inverse Problems
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    Chapter 3 Algebraic Reconstruction of Source and Attenuation in SPECT Using First Scattering Measurements
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    Chapter 4 On ℓ 1 -Regularization Under Continuity of the Forward Operator in Weaker Topologies
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    Chapter 5 On Self-regularization of Ill-Posed Problems in Banach Spaces by Projection Methods
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    Chapter 6 Monotonicity-Based Regularization for Phantom Experiment Data in Electrical Impedance Tomography
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    Chapter 7 An SVD in Spherical Surface Wave Tomography
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    Chapter 8 Numerical Studies of Recovery Chances for a Simplified EIT Problem
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    Chapter 9 Bayesian Updating in the Determination of Forces in Euler-Bernoulli Beams
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    Chapter 10 On Nonstationary Iterated Tikhonov Methods for Ill-Posed Equations in Banach Spaces
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    Chapter 11 The Product Midpoint Rule for Abel-Type Integral Equations of the First Kind with Perturbed Data
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    Chapter 12 Heuristic Parameter Choice in Tikhonov Method from Minimizers of the Quasi-Optimality Function
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    Chapter 13 Modification of Iterative Tikhonov Regularization Motivated by a Problem of Identification of Laser Beam Quality Parameters
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    Chapter 14 Tomographic Terahertz Imaging Using Sequential Subspace Optimization
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    Chapter 15 Adaptivity and Oracle Inequalities in Linear Statistical Inverse Problems: A (Numerical) Survey
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    Chapter 16 Relaxing Alternating Direction Method of Multipliers (ADMM) for Linear Inverse Problems
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Citations

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Title
New Trends in Parameter Identification for Mathematical Models
Published by
Springer International Publishing, February 2018
DOI 10.1007/978-3-319-70824-9
ISBNs
978-3-31-970823-2, 978-3-31-970824-9
Editors

Hofmann, Bernd, Leitão, Antonio, Zubelli, Jorge P.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 27%
Professor > Associate Professor 2 18%
Student > Doctoral Student 2 18%
Lecturer 1 9%
Student > Master 1 9%
Other 1 9%
Unknown 1 9%
Readers by discipline Count As %
Mathematics 7 64%
Computer Science 1 9%
Engineering 1 9%
Unknown 2 18%