The presentation shows sample paths of stochastic processes in form of animations. Those stochastic procsses are usually used to model financial quantities like exchange rates, interest rates and stock prices. In the second part the solution of the Black-Scholes PDE using the finite difference method is illustrated. / Der Vortrag zeigt Animationen von Realisierungen stochstischer Prozesse, die zur Modellierung von Groessen im Finanzbereich haeufig verwendet werden (z.B. Wechselkurse, Zinskurse, Aktienkurse). Im zweiten Teil wird die Loesung der Black-Scholes Partiellen Differentialgleichung mittels Finitem Differenzenverfahren graphisch veranschaulicht.

Continuing the previous work in the preprint 97-11 done for the 2D-approach in this paper we describe the Yserentant preconditioned conjugate gradient method as well as the BPX-preconditioned cg-iteration fastly solving 3D-elliptic boundary value problems on unstructured quasi uniform grids. These artificially constructed hierarchical methods have optimal computational costs. In the case of the sequential computing several numerical examples demonstrate their efficiency not depending on the finite element types used for the discretiziation of the original potential problem. Moreover, implementing the methods in parallel first results are given.

Matrix Lyapunov and Riccati equations are an important tool in mathematical systems theory. They are the key ingredients in balancing based model order reduction techniques and linear quadratic regulator problems. For small and moderately sized problems these equations are solved by techniques with at least cubic complexity which prohibits their usage in large scale applications. Around the year 2000 solvers for large scale problems have been introduced. The basic idea there is to compute a low rank decomposition of the quadratic and dense solution matrix and in turn reduce the memory and computational complexity of the algorithms. In this thesis efficiency enhancing techniques for the low rank alternating directions implicit iteration based solution of large scale matrix equations are introduced and discussed. Also the applicability in the context of real world systems is demonstrated. The thesis is structured in seven central chapters. After the introduction chapter 2 introduces the basic concepts and notations needed as fundamental tools for the remainder of the thesis. The next chapter then introduces a collection of test examples spanning from easily scalable academic test systems to badly conditioned technical applications which are used to demonstrate the features of the solvers. Chapter four and five describe the basic solvers and the modifications taken to make them applicable to an even larger class of problems. The following two chapters treat the application of the solvers in the context of model order reduction and linear quadratic optimal control of PDEs. The final chapter then presents the extensive numerical testing undertaken with the solvers proposed in the prior chapters. Some conclusions and an appendix complete the thesis.

In this paper we discuss the identification of parameter functions in material models for elastic large deformations. A model of the the forward problem is given, where the displacement of a deformed material is found as the solution of a n onlinear PDE. Here, the crucial point is the definition of the 2nd Piola-Kirchhoff stress tensor by using several material laws including a number of material parameters. In the main part of the paper we consider the identification of such parameters from measured displacements, where the inverse problem is given as an optimal control problem. We introduce a solution of the identification problem with Lagrange and SQP methods. The presented algorithm is applied to linear elastic material with large deformations.

The analytical solution of a given inhomogeneous boundary value problem of a linear, elliptic, inhomogeneous partial differential equation and a set of inhomogeneous mixed Dirichlet- and Neumann-type boundary conditions is derived in the present paper. In the context of elasticity theory, the problem arises for a non-conservative symmetric ansatz and an extended constitutive law shown earlier. For convenient user application, the scalar function expressed in cylindrical coordinates is primarily obtained for the general case before being expatiated on a special case of linear boundary conditions.

The discretization of the stationary diffusion equation with random parameters by the Stochastic Finite Element Method requires the solution of a highly structured but very large linear system of equations. Depending on the stochastic properties of the diffusion coefficient together with the stochastic discretization we consider three solver cases. If the diffusion coefficient is given by a stochastically linear expansion, e.g. a truncated Karhunen-Loeve expansion, and tensor product polynomial stochastic shape functions are employed, the Galerkin matrix can be transformed to a block-diagonal matrix. For the solution of the resulting sequence of linear systems we study Krylov subspace recycling methods whose success depends on the ordering and grouping of the linear systems as well as the preconditioner. If we use complete polynomials for the stochastic discretization instead, we show that decoupling of the Galerkin matrix with respect to the stochastic degrees of freedom is impossible. For a stochastically nonlinear diffusion coefficient, e.g. a lognormal random field, together with complete polynomials serving as stochastic shape functions, we introduce and test the performance of a new Kronecker product preconditioner, which is not exclusively based on the mean value of the diffusion coefficient.

In addition to previous publications, the paper presents the analytical solution of a special boundary value problem which arises in the context of elasticity theory for an extended constitutive law and a non-conservative symmetric ansatz. Besides deriving the general analytical solution, a specific form for linear boundary conditions is given for user convenience.

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The focus of this paper is on conditional stability estimates for ill-posed inverse problems in partial differential equations. Conditional stability estimates have been obtained in the literature by a couple different methods. In this paper we propose a method called interpolation method, which is based on interpolation in variable Hilbert scales. We are going to work out the theoretical background of this method and show that optimal conditional stability estimates are obtained. The capability of our method is illustrated by a comprehensive collection of different inverse and ill-posed PDE problems containing elliptic and parabolic problems, one source problem and the problem of analytic continuation.

Die Dissertation beschäftigt sich mit Parameteridentifikationsproblemen, wie sie häufig in Fragestellungen der Festkörpermechanik zu finden sind. Hierbei betrachten wir die Identifikation von Materialparametern -- die typischerweise die Eigenschaften der zugrundeliegenden Materialien repräsentieren -- aus gemessenen Verformungen oder Belastungen eines Testkörpers. In mathematischem Sinne entspricht dies der Lösung von Identifikationsproblemen, die eine spezielle Klasse von inversen Problemen bilden. Der Inhalt der Dissertation ist folgendermaßen gegliedert. Nach dem einführenden Abschnitt 1 wird in Abschnitt 2 ein Überblick von Optimierungs- und Regularisierungsverfahren zur stabilen Lösung nichtlinearer inverser Probleme diskutiert. In Abschnitt 3 betrachten wir die Identifikation von skalaren und stückweise konstanten Parametern in linearen elliptischen Differentialgleichungen. Hierbei werden zwei Testprobleme erörtert, die Identifikation von Diffusions- und Reaktionsparameter in einer allgemeinen elliptischen Differentialgleichung und die Identifikation der Lame-Konstanten in einem Modell der linearisierten Elastizität. Die zugrunde liegenden PDE-Modelle und Lösungszugänge werden erläutert. Insbesondere betrachten wir hier Newton-artige Algorithmen, Gradientenmethoden, Multi-Parameter Regularisierung and den evolutionären Algorithmus CMAES. Abschließend werden Ergebnisse einer numerischen Studie präsentiert. Im Abschnitt 4 konzentrieren wir uns auf die Identifikation von verteilten Parametern in hyperelastischen Materialmodellen. Das nichtlineare Elastizitätsproblem wird detailiert erläutert und verschiedene Materialmodelle werden diskutiert (linear elastisches St.-Venant-Kirchhoff Material und nichtlineare Neo-Hooke, Mooney-Rivlin und Modified-Fung Materialien. Zur Lösung des resultierenden Parameteridentifikationsproblems werden Lösungsansätze aus der optimalen Steuerung in Form eines Newton-Lagrange SQP Algorithmus verwendet. Die Resultate einer numerischen Studie werden präsentiert, basierend auf einem zweidimensionales Testproblem mit einer sogenannten Cook-Mebran. Abschließend wird im Abschnitt 5 die Verwendung adaptiver FEM für die Lösung von Parameteridentifikationsproblems kurz erörtert. / The dissertation is focussed on parameter identification problems arising in the context of structural mechanics. At this, we consider the identification of material parameters - which typically represent the properties of an underlying material - from given measured displacements and forces of a loaded test body. In mathematical terms such problems denote identification problems as a special case of general inverse problems. The dissertation is organized as follows. After the introductive section 1, section 2 is devoted to a survey of optimization and regularization methods for the stable solution of nonlinear inverse problems. In section 3 we consider the identification of scalar and piecewise constant parameters in linear elliptic differential equations and examine two test problems, namely the identification of diffusion and reaction parameters in a generalized linear elliptic differential equation of second order and the identification of the Lame constants in the linearized elasticity model. The underlying PDE models are introduced and solution approaches are discussed in detail. At this, we consider Newton-type algorithms, gradient methods, multi-parameter regularization, and the evolutionary algorithm CMAES. Consequently, numerical studies for a two-dimensional test problem are presented. In section 4 we point out the identification of distributed material parameters in hyperelastic deformation models. The nonlinear elasticity boundary value problem for large deformations is introduced. We discuss several material laws for linear elastic (St.-Venant-Kirchhoff) materials and nonlinear Neo-Hooke, Mooney-Rivlin, and Modified-Fung materials. For the solution of the corresponding parameter identification problem, we focus on an optimal control solution approach and introduce a regularized Newton-Lagrange SQP method. The Newton-Lagrange algorithm is demonstrated within a numerical study. Therefore, a simplified two-dimensional Cook membrane test problem is solved. Additionally, in section 5 the application of adaptive methods for the solution of parameter identification problems is discussed briefly.