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- Visualisierung (3)
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- Copula (1)
- Credit Default Swap (1)
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Due to remarkable technological advances in the last three decades the capacity of computer systems has improved tremendously. Considering Moore's law, the number of transistors on integrated circuits has doubled approximately every two years and the trend is continuing. Likewise, developments in storage density, network bandwidth, and compute capacity show similar patterns. As a consequence, the amount of data that can be processed by today's systems has increased by orders of magnitude. At the same time, however, the resolution of screens has hardly increased by a factor of ten. Thus, there is a gap between the amount of data that can be processed and the amount of data that can be visualized. Large high-resolution displays offer a way to deal with this gap and provide a significantly increased screen area by combining the images of multiple smaller display devices. The main objective of this dissertation is the development of new visualization and interaction techniques for large high-resolution displays.

In this work two main approaches for the evaluation of credit derivatives are analyzed: the copula based approach and the Markov Chain based approach. This work gives the opportunity to use the advantages and avoid disadvantages of both approaches. For example, modeling of contagion effects, i.e. modeling dependencies between counterparty defaults, is complicated under the copula approach. One remedy is to use Markov Chain, where it can be done directly. The work consists of five chapters. The first chapter of this work extends the model for the pricing of CDS contracts presented in the paper by Kraft and Steffensen (2007). In the widely used models for CDS pricing it is assumed that only borrower can default. In our model we assume that each of the counterparties involved in the contract may default. Calculated contract prices are compared with those calculated under usual assumptions. All results are summarized in the form of numerical examples and plots. In the second chapter the copula and its main properties are described. The methods of constructing copulas as well as most common copulas families and its properties are introduced. In the third chapter the method of constructing a copula for the existing Markov Chain is introduced. The cases with two and three counterparties are considered. Necessary relations between the transition intensities are derived to directly find some copula functions. The formulae for default dependencies like Spearman's rho and Kendall's tau for defined copulas are derived. Several numerical examples are presented in which the copulas are built for given Markov Chains. The fourth chapter deals with the approximation of copulas if for a given Markov Chain a copula cannot be provided explicitly. The fifth chapter concludes this thesis.

A main result of this thesis is a conceptual proof of the fact that the weighted number of tropical curves of given degree and genus, which pass through the right number of general points in the plane (resp., which pass through general points in R^r and represent a given point in the moduli space of genus g curves) is independent of the choices of points. Another main result is a new correspondence theorem between plane tropical cycles and plane elliptic algebraic curves.

We discuss some first steps towards experimental design for neural network regression which, at present, is too complex to treat fully in general. We encounter two difficulties: the nonlinearity of the models together with the high parameter dimension on one hand, and the common misspecification of the models on the other hand.
Regarding the first problem, we restrict our consideration to neural networks with only one and two neurons in the hidden layer and a univariate input variable. We prove some results regarding locally D-optimal designs, and present a numerical study using the concept of maximin optimal designs.
In respect of the second problem, we have a look at the effects of misspecification on optimal experimental designs.

For computational reasons, the spline interpolation of the Earth's gravitational potential is usually done in a spherical framework. In this work, however, we investigate a spline method with respect to the real Earth. We are concerned with developing the real Earth oriented strategies and methods for the Earth's gravitational potential determination. For this purpose we introduce the reproducing kernel Hilbert space of Newton potentials on and outside given regular surface with reproducing kernel defined as a Newton integral over it's interior. We first give an overview of thus far achieved results considering approximations on regular surfaces using surface potentials (Chapter 3). The main results are contained in the fourth chapter where we give a closer look to the Earth's gravitational potential, the Newton potentials and their characterization in the interior and the exterior space of the Earth. We also present the L2-decomposition for regions in R3 in terms of distributions, as a main strategy to impose the Hilbert space structure on the space of potentials on and outside a given regular surface. The properties of the Newton potential operator are investigated in relation to the closed subspace of harmonic density functions. After these preparations, in the fifth chapter we are able to construct the reproducing kernel Hilbert space of Newton potentials on and outside a regular surface. The spline formulation for the solution to interpolation problems, corresponding to a set of bounded linear functionals is given, and corresponding convergence theorems are proven. The spline formulation reflects the specifics of the Earth's surface, due to the representation of the reproducing kernel (of the solution space) as a Newton integral over the inner space of the Earth. Moreover, the approximating potential functions have the same domain of harmonicity as the actual Earth's gravitational potential, i.e., they are harmonic outside and continuous on the Earth's surface. This is a step forward in comparison to the spherical harmonic spline formulation involving functions harmonic down to the Runge sphere. The sixth chapter deals with the representation of the used kernel in the spherical case. It turns out that in the case of the spherical Earth, this kernel can be considered a kind of generalization to spherically oriented kernels, such as Abel-Poisson or the singularity kernel. We also investigate the existence of the closed expression of the kernel. However, at this point it remains to be unknown to us. So, in Chapter 7, we are led to consider certain discretization methods for integrals over regions in R3, in connection to theory of the multidimensional Euler summation formula for the Laplace operator. We discretize the Newton integral over the real Earth (representing the spline function) and give a priori estimates for approximate integration when using this discretization method. The last chapter summarizes our results and gives some directions for the future research.

The recognition of patterns and structures has gained importance for dealing with the growing amount of data being generated by sensors and simulations. Most existing methods for pattern recognition are tailored for scalar data and non-correlated data of higher dimensions. The recognition of general patterns in flow structures is possible, but not yet practically usable, due to the high computation effort. The main goal of this work is to present methods for comparative visualization of flow data, amongst others, based on a new method for efficient pattern recognition on flow data. This work is structured in three parts: At first, a known feature-based approach for pattern recognition on flow data, the Clifford convolution, has been applied to color edge detection, and been extended to non-uniform grids. However, this method is still computationally expensive for a general pattern recognition, since the recognition algorithm has to be applied for numerous different scales and orientations of the query pattern. A more efficient and accurate method for pattern recognition on flow data is presented in the second part. It is based upon a novel mathematical formulation of moment invariants for flow data. The common moment invariants for pattern recognition are not applicable on flow data, since they are only invariant on non-correlated data. Because of the spatial correlation of flow data, the moment invariants had to be redefined with different basis functions to satisfy the demands for an invariant mapping of flow data. The computation of the moment invariants is done by a multi-scale convolution of the complete flow field with the basis functions. This pre-processing computation time almost equals the time for the pattern recognition of one single general pattern with the former algorithms. However, after having computed the moments once, they can be indexed and used as a look-up-table to recognize any desired pattern quickly and interactively. This results in a flexible and easy-to-use tool for the analysis of patterns in 2d flow data. For an improved rendering of the recognized features, an importance driven streamline algorithm has been developed. The density of the streamlines can be adjusted by using importance maps. The result of a pattern recognition can be used as such a map, for example. Finally, new comparative flow visualization approaches utilizing the streamline approach, the flow pattern matching, and the moment invariants are presented.

Numerical Algorithms in Algebraic Geometry with Implementation in Computer Algebra System SINGULAR
(2011)

Polynomial systems arise in many applications: robotics, kinematics, chemical kinetics,
computer vision, truss design, geometric modeling, and many others. Many polynomial
systems have solutions sets, called algebraic varieties, having several irreducible
components. A fundamental problem of the numerical algebraic geometry is to decompose
such an algebraic variety into its irreducible components. The witness point sets are
the natural numerical data structure to encode irreducible algebraic varieties.
Sommese, Verschelde and Wampler represented the irreducible algebraic decomposition of
an affine algebraic variety \(X\) as a union of finite disjoint sets \(\cup_{i=0}^{d}W_i=\cup_{i=0}^{d}\left(\cup_{j=1}^{d_i}W_{ij}\right)\) called numerical irreducible decomposition. The \(W_i\) correspond to the pure i-dimensional components, and the \(W_{ij}\) represent the i-dimensional irreducible components. The numerical irreducible decomposition is implemented in BERTINI.
We modify this concept using partially Gröbner bases, triangular sets, local dimension, and
the so-called zero sum relation. We present in the second chapter the corresponding
algorithms and their implementations in SINGULAR. We give some examples and timings,
which show that the modified algorithms are more efficient if the number of variables is not
too large. For a large number of variables BERTINI is more efficient.
Leykin presented an algorithm to compute the embedded components of an algebraic variety
based on the concept of the deflation of an algebraic variety.
Depending on the modified algorithm mentioned above, we will present in the third chapter an
algorithm and its implementation in SINGULAR to compute the embedded components.
The irreducible decomposition of algebraic varieties allows us to formulate in the fourth
chapter some numerical algebraic algorithms.
In the last chapter we present two SINGULAR libraries. The first library is used to compute
the numerical irreducible decomposition and the embedded components of an algebraic variety.
The second library contains the procedures of the algorithms in the last Chapter to test
inclusion, equality of two algebraic varieties, to compute the degree of a pure i-dimensional
component, and the local dimension.

This thesis treats the extension of the classical computational homogenization scheme towards the multi-scale computation of material quantities like the Eshelby stresses and material forces. To this end, microscopic body forces are considered in the scale-transition, which may emerge due to inhomogeneities in the material. Regarding the determination of material quantities based on the underlying microscopic structure different approaches are compared by means of their virtual work consistency. In analogy to the homogenization of spatial quantities, this consistency is discussed within Hill-Mandel type conditions.

Multi-Field Visualization
(2011)

Modern science utilizes advanced measurement and simulation techniques to analyze phenomena from fields such as medicine, physics, or mechanics. The data produced by application of these techniques takes the form of multi-dimensional functions or fields, which have to be processed in order to provide meaningful parts of the data to domain experts. Definition and implementation of such processing techniques with the goal to produce visual representations of portions of the data are topic of research in scientific visualization or multi-field visualization in the case of multiple fields. In this thesis, we contribute novel feature extraction and visualization techniques that are able to convey data from multiple fields created by scientific simulations or measurements. Furthermore, our scalar-, vector-, and tensor field processing techniques contribute to scattered field processing in general and introduce novel ways of analyzing and processing tensorial quantities such as strain and displacement in flow fields, providing insights into field topology. We introduce novel mesh-free extraction techniques for visualization of complex-valued scalar fields in acoustics that aid in understanding wave topology in low frequency sound simulations. The resulting structures represent regions with locally minimal sound amplitude and convey wave node evolution and sound cancellation in time-varying sound pressure fields, which is considered an important feature in acoustics design. Furthermore, methods for flow field feature extraction are presented that facilitate analysis of velocity and strain field properties by visualizing deformation of infinitesimal Lagrangian particles and macroscopic deformation of surfaces and volumes in flow. The resulting adaptive manifolds are used to perform flow field segmentation which supports multi-field visualization by selective visualization of scalar flow quantities. The effects of continuum displacement in scattered moment tensor fields can be studied by a novel method for multi-field visualization presented in this thesis. The visualization method demonstrates the benefit of clustering and separate views for the visualization of multiple fields.

This thesis is concerned with the modeling of the domain structure evolution in ferroelectric materials. Both a sharp interface model, in which the driving force on a domain wall is used to postulate an evolution law, and a continuum phase field model are treated in a thermodynamically consistent framework. Within the phase field model, a Ginzburg-Landau type evolution law for the spontaneous polarization is derived. Numerical simulations (FEM) show the influence of various kinds of defects on the domain wall mobility in comparison with experimental findings. A macroscopic material law derived from the phase field model is used to calculate polarization yield surfaces for multiaxial loading conditions.