Titlebook: Regularization for Applied Inverse and Ill-Posed Problems; A Numerical Approach Bernd Hofmann (associate professor in numerical ma Textbook

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書目名稱Regularization for Applied Inverse and Ill-Posed Problems讀者反饋




書目名稱Regularization for Applied Inverse and Ill-Posed Problems讀者反饋學(xué)科排名

豐富

使困惑

A Unified Numerical Approach to Nonlinear Inverse Problems,e learned that identification and control problems are regularized in a unified manner. Consequently, for our classification, we only have to consider the space dimensions m and n of the discretized inverse problem and intrinsic features of the operator A and of the domain D.

bleach

Introduction,ne can be checked without implementing the expensive process or machinery hardware. Many developments in the fields of numerical mathematics, computer science, analysis, mechanics, system theory etc. have been stimulated by the requirements of practice regarding simulation experiments.

折磨

Regularization of Deterministic Discretized Inverse Problems,under consideration. The idea of dealing with this family of problems goes back to TIKHONOV [428–29]. Therefore, the method considered below in a particular fashion is called Tikhonov regularization method.

Lignans

Introduction,ter simulation of real processes. The economic advantage of simulating the behaviour of physical field quantities (e.g. temperature, pressure, stress, velocity, etc.), varying in space and in time, by a digital computer is considerable. Desired properties of a process or desired reactions of a machi

蠟燭

A General Optimization Approach,s continuously depend upon the input data. In this Chapter 3, we only consider the strictly deterministic and non-Bayesian case (see Sec. 2,3.). The study of the present section deals with the semi-discretization model, i.e., the determination of Banach or Hilbert space elements from an m-dimensiona

制定

Regularization of Deterministic Discretized Inverse Problems,ithin Chapter 3, auxiliary problems of minimization type (3.39) and (3–40) have been constructed, the solutions of which continuously depend on the input data. Now we present a family of well-posed optimization problems that represent stable neighbouring problems for the discretized inverse problem

內(nèi)向者

Regularization of Stochastic Discretized Inverse Problems,ation method. From the numerical point of view the stochastic approach to discretized inverse problems was also of interest in the ensuing years (see PETROV [351], TURCHIN ot al. [448], FRANKLIN [130], FRIEDRICH et al. [138] and FEDOTOV [1281). In this chapter, we are going to present some main idea

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