Thèse doctorat : Automatique, Traitement du Signal et Génie Informatique : Nancy 1 : 2005. / Titre provenant de l'écran-titre.

In this paper two basic SQP-approaches for solving implicitly defined inverse problems are presented. Such problems often arises in parameter identification for differential equations. We also include regularization strategies which differ from similar problems in Optimal control. The main focus is on formulating saddle point problems for calculating the next iterate. Conditions for the unique and stable solvability of these problems are presented. The analytical considerations are illustrated by two examples including their discretizations and a numerical case study.

In this paper we discuss the inverse problem of the identification of mechanical stresses by measuring the deformation of an electric potential field in a so called differential strain gauge (D-DMS). We derive a mathematical model, where the forward operator is given in terms of an elliptic boundary value problem. Derivatives of the forward operator are considered and the solution of the inverse problem via a least-squares minimization is introduced. Here, the discretized problem is solved with the Gauss-Newton method. Numerical studies of practical interest are presented.

In this paper we continue the considerations of [5] (CSC/09-05). A numerical study for the parameter identification problem with linear elastic material and large deformations is presented. We discuss the numerical implementation in MATLAB and illustrate some results for a 2D test problem.

No description available.

We are investigating a kernel-based autoconvolution problem, which has its origin in the physics of ultra short laser pulses. The task in this problem is to reconstruct a complex-valued function $x$ on a finite interval from measurements of its absolute value and a kernel-based autoconvolution of the form [[F(x)](s)=int k(s,t)x(s-t)x(t)de t.] This problem has not been studied in the literature. One reason might be that one has more information than in the classical autoconvolution case, where only the right hand side is available. Nevertheless we show that ill posedness phenomena may occur. We also propose an algorithm to solve the problem numerically and demonstrate its performance with artificial data. Since the algorithm fails to produce good results with real data and we suspect that the data for $|F(x)|$ are not dependable we also consider the whole problem with only $arg(F(x))$ given instead of $F(x)$.

Heidelberg, Univ., Diss., 2006.

Th. univ.--Sci. phys. de la terre--Paris VII, 1987. / Notes bibliogr.

Th. doct.--Math.-autom.--Paris IX, 1991. / Bibliogr. p. 289-295.

Th. univ.--Math. appliquées--Paris 13, 1995. / 1995 d'après la déclaration de dépôt légal. Textes en français ou en anglais. Bibliogr. p. 149-152. Résumé bilingue.