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NONZONAL WAVELETS ON S^N
(2010)

In the present article we will construct wavelets on an arbitrary dimensional sphere S^n due the approach of approximate Identities. There are two equivalently approaches to wavelets. The group theoretical approach formulates a square integrability condition for a group acting via unitary, irreducible representation on the sphere. The connection to the group theoretical approach will be sketched. The concept of approximate identities uses the same constructions in the background, here we select an appropriate section of dilations and translations in the group acting on the sphere in two steps. At First we will formulate dilations in terms of approximate identities and than we call in translations on the sphere as rotations. This leads to the construction of an orthogonal polynomial system in L²(SO(n+1)). That approach is convenient to construct concrete wavelets, since the appropriate kernels can be constructed form the heat kernel leading to the approximate Identity of Gauss-Weierstra\ss. We will work out conditions to functions forming a family of wavelets, subsequently we formulate how we can construct zonal wavelets from a approximate Identity and the relation to admissibility of nonzonal wavelets. Eventually we will give an example of a nonzonal Wavelet on $S^n$, which we obtain from the approximate identity of Gauss-Weierstraß.

Maxwell's equations can be rewritten in terms of a Dirac operator D+a. The advantage is that in this setting Maxwell's equations are treated as a system of first order differential equations. To ensure the uniqueness of a non-homogeneous differential equation in the whole space additional conditions are needed.

A realistic and reliable model is an important precondition for the simulation of revitalization tasks and the estimation of system properties of existing buildings. Thereby, the main focus lies on the parameter identification, the optimization strategies and the preparation of experiments. As usual structures are modeled by the finite element method. This as well as other techniques are based on idealizations and empiric material properties. Within one theory the parameters of the model should be approximated by gradually performed experiments and their analysis. This approximation method is performed by solving an optimization problem, which is usually non-convex, of high dimension and possesses a non-differentiable objective function. Therefore we use an optimization procedure based on genetic algorithms which was implemented by using the program package SLang...