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Inverse and Ill-posed Problems

interest. The concept of ill-posed problems was introduced by Hadamard with the comment that these problems are physically meaningless and not;

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Optimal Methods for Ill-Posed Problems

The book covers fundamentals of the theory of optimal methods for solving ill-posed problems, as well as ways to obtain accurate and;

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Advanced Mechatronics

thermal systems. Finally, indirect and remote monitoring and control issues are analyzed as cases of ill-posed inverse problems. Numerous;

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Regularization Algorithms for Ill-Posed Problems

This specialized and authoritative book contains an overview of modern approaches to constructing approximations to solutions of ill-posed;

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An Introduction to the Mathematical Theory of Inverse Problems

ill-posed problems, analyzing basic properties of regularization methods for ill-posed problems via several simple analytical and numerical;

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An Introduction to the Mathematical Theory of Inverse Problems

ill-posed problems, analyzing basic properties of regularization methods for ill-posed problems via several simple analytical and numerical;

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Inverse Problems and Applications

analytical developments and numerical methods for solutions of inverse and ill-posed problems, which consistently pose complex challenges to the;

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Rank-Deficient and Discrete Ill-Posed Problems

for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties;

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Mathematical and Numerical Approaches for Multi-Wave Inverse Problems

, and geophysics, to name but a few. In turn, it is well known that inverse problems are both nonlinear and ill-posed: two factors that pose;

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Inverse Problems in Engineering Mechanics III

problems are ill-posed and have to be overcome through development of new computational schemes, regularization techniques, objective functionals;

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Inverse Stefan Problems

boundaries. This new approach to the theory of ill-posed problems is useful for the modelling of nonlinear processes with phase transforms;

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Inverse Problems in Physical Diagnostics

. The solution of integral equations is, with few exceptions, an ill-posed problem, and additional a priori information about the exact solution;

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New Trends in Parameter Identification for Mathematical Models

regularization methods in Hilbert and Banach spaces for the stable approximate solution of ill-posed inverse problems, novel methods for parameter;

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Dynamical Systems Method for Solving Nonlinear Operator Equations

analysis, and ill-posed and inverse problems especially. The book presents a general method for solving operator equations, especially nonlinear;

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Bayesian Approach to Inverse Problems

recover a quantity of interest from measurable data. Unfortunately, most inverse problems are ill-posed, which means that precise and stable;

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Advances in Computational Mathematics

, computational approximation, numerical solutions of differential and integral equations, numerical linear algebra, inverse and ill-posed problems;

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Nonlinear and Inverse Problems in Electromagnetics

nonlinear and ill-posed nature of inverse problems and the challenges they present when developing new numerical methods are explained, and;

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Mathematical and Computational Sciences

etc. Most of the real-time problems are inverse in nature and among them many are ill-posed. Such problems are a matter of concern among the;

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Introduction to Inverse Problems for Differential Equations

used as the backbone for a lecture course on inverse and ill-posed problems for partial differential equations. In turn, the second part;

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Introduction to Inverse Problems for Differential Equations

used as the backbone for a lecture course on inverse and ill-posed problems for partial differential equations. In turn, the second part;

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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Conjugate Gradient Type Methods for Ill-Posed Problems

) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including;

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Inverse Problems in the Mathematical Sciences

, posed as coefficient determination problems in differential equations; the functional analytic framework for inverse problems; and a survey;

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An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems

problems. The author covers basic statistical inference, introduces the framework of ill-posed inverse problems, and explains statistical questions;

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Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry

Numerical Methods of Solving Ill-Posed Problems of Dielectric Spectrometry;

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Spline Models for Observational Data

within this framework. Methods for including side conditions and other prior information in solving ill posed inverse problems are included. Data;

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Variational Regularization for Systems of Inverse Problems

an approach allows to choose the regularization weights of each subproblem individually with respect to the corresponding noise levels and;

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