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In recent years special hypercomplex Appell polynomials have been introduced by several authors and their main properties have been studied by different methods and with different objectives. Like in the classical theory of Appell polynomials, their generating function is a hypercomplex exponential function. The observation that this generalized exponential function has, for example, a close relationship with Bessel functions confirmed the practical significance of such an approach to special classes of hypercomplex differentiable functions. Its usefulness for combinatorial studies has also been investigated. Moreover, an extension of those ideas led to the construction of complete sets of hypercomplex Appell polynomial sequences. Here we show how this opens the way for a more systematic study of the relation between some classes of Special Functions and Elementary Functions in Hypercomplex Function Theory.
The numerical simulation of microstructure models in 3D requires, due to enormous d.o.f., significant resources of memory as well as parallel computational power. Compared to homogeneous materials, the material hetrogeneity on microscale induced by different material phases demand for adequate computational methods for discretization and solution process of the resulting highly nonlinear problem. To enable an efficient/scalable solution process of the linearized equation systems the heterogeneous FE problem will be described by a FETI-DP (Finite Element Tearing and Interconnecting - Dual Primal) discretization. The fundamental FETI-DP equation can be solved by a number of different approaches. In our approach the FETI-DP problem will be reformulated as Saddle Point system, by eliminating the primal and Lagrangian variables. For the reduced Saddle Point system, only defined by interior and dual variables, special Uzawa algorithms can be adapted for iteratively solving the FETI-DP saddle-point equation system (FETI-DP SPE). A conjugate gradient version of the Uzawa algorithm will be shown as well as some numerical tests regarding to FETI-DP discretization of small examples using the presented solution technique. Furthermore the inversion of the interior-dual Schur complement operator can be approximated using different techniques building an adequate preconditioning matrix and therewith leading to substantial gains in computing time efficiency.
What is nowadays called (classic) Clifford analysis consists in the establishment of a function theory for functions belonging to the kernel of the Dirac operator. While such functions can very well describe problems of a particle with internal SU(2)-symmetries, higher order symmetries are beyond this theory. Although many modifications (such as Yang-Mills theory) were suggested over the years they could not address the principal problem, the need of a n-fold factorization of the d’Alembert operator. In this paper we present the basic tools of a fractional function theory in higher dimensions, for the transport operator (alpha = 1/2 ), by means of a fractional correspondence to the Weyl relations via fractional Riemann-Liouville derivatives. A Fischer decomposition, fractional Euler and Gamma operators, monogenic projection, and basic fractional homogeneous powers are constructed.
The aim of this paper we discuss explicit series constructions for the fundamental solution of the Helmholtz operator on some important examples non-orientable conformally at manifolds. In the context of this paper we focus on higher dimensional generalizations of the Klein bottle which in turn generalize higher dimensional Möbius strips that we discussed in preceding works. We discuss some basic properties of pinor valued solutions to the Helmholtz equation on these manifolds.
This research focuses on an approach to describe principles in architectural layout planning within the domain of revitalization. With the aid of mathematical rules, which are executed by a computer, solutions to design problems are generated. Provided that "design" is in principle a combinatorial problem, i.e. a constraint-based search for an overall optimal solution of a problem, an exemplary method will be described to solve such problems in architectural layout planning. To avoid conflicts relating to theoretical subtleness, a customary approach adopted from Operations Research has been chosen in this work. In this approach, design is a synonym for planning, which could be described as a systematic and methodical course of action for the analysis and solution of current or future problems. The planning task is defined as an analysis of a problem with the aim to prepare optimal decisions by the use of mathematical methods. The decision problem of a planning task is represented by an optimization model and the application of an efficient algorithm in order to aid finding one or more solutions to the problem. The basic principle underlying the approach presented herein is the understanding of design in terms of searching for solutions that fulfill specific criteria. This search is executed by the use of a constraint programming language.
The paper is dedicated to decidability exploration of market segmentation problem with the help of linear convolution algorithms. Mathematical formulation of this problem represents interval task of bipartite graph cover by stars. Vertices of the first partition correspond to types of commodities, vertices of the second – to customers groups. Appropriate method is offered for interval problem reduction to two-criterion task that has one implemented linear convolution algorithm. Unsolvability with the help of linear convolution algorithm of multicriterion, and consequently interval, market segmentation problem is proved.
We consider efficient numerical methods for the solution of partial differential equations with stochastic coefficients or right hand side. The discretization is performed by the stochastic finite element method (SFEM). Separation of spatial and stochastic variables in the random input data is achieved via a Karhunen-Loève expansion or Wiener's polynomial chaos expansion. We discuss solution strategies for the Galerkin system that take advantage of the special structure of the system matrix. For stochastic coefficients linear in a set of independent random variables we employ Krylov subspace recycling techniques after having decoupled the large SFEM stiffness matrix.
The contribution presents a model that is able to simulate construction duration and cost for a building project. This model predicts set of expected project costs and duration schedule depending on input parameters such as production speed, scope of work, time schedule, bonding conditions and maximum and minimum deviations from scope of work and production speed. The simulation model is able to calculate, on the basis of input level of probability, the adequate construction cost and time duration of a project. The reciprocal view attends to finding out the adequate level of probability for construction cost and activity durations. Among interpretive outputs of the application software belongs the compilation of a presumed dynamic progress chart. This progress chart represents the expected scenario of development of a building project with the mapping of potential time dislocations for particular activities. The calculation of a presumed dynamic progress chart is based on an algorithm, which calculates mean values as a partial result of the simulated building project. Construction cost and time models are, in many ways, useful tools in project management. Clients are able to make proper decisions about the time and cost schedules of their investments. Consequently, building contractors are able to schedule predicted project cost and duration before any decision is finalized.
Within the scheduling of construction projects, different, partly conflicting objectives have to be considered. The specification of an efficient construction schedule is a challenging task, which leads to a NP-hard multi-criteria optimization problem. In the past decades, so-called metaheuristics have been developed for scheduling problems to find near-optimal solutions in reasonable time. This paper presents a Simulated Annealing concept to determine near-optimal construction schedules. Simulated Annealing is a well-known metaheuristic optimization approach for solving complex combinatorial problems. To enable dealing with several optimization objectives the Pareto optimization concept is applied. Thus, the optimization result is a set of Pareto-optimal schedules, which can be analyzed for selecting exactly one practicable and reasonable schedule. A flexible constraint-based simulation approach is used to generate possible neighboring solutions very quickly during the optimization process. The essential aspects of the developed Pareto Simulated Annealing concept are presented in detail.
A practical framework for generating cross correlated fields with a specified marginal distribution function, an autocorrelation function and cross correlation coefficients is presented in the paper. The contribution promotes a recent journal paper [1]. The approach relies on well known series expansion methods for simulation of a Gaussian random field. The proposed method requires all cross correlated fields over the domain to share an identical autocorrelation function and the cross correlation structure between each pair of simulated fields to be simply defined by a cross correlation coefficient. Such relations result in specific properties of eigenvectors of covariance matrices of discretized field over the domain. These properties are used to decompose the eigenproblem which must normally be solved in computing the series expansion into two smaller eigenproblems. Such decomposition represents a significant reduction of computational effort. Non-Gaussian components of a multivariate random field are proposed to be simulated via memoryless transformation of underlying Gaussian random fields for which the Nataf model is employed to modify the correlation structure. In this method, the autocorrelation structure of each field is fulfilled exactly while the cross correlation is only approximated. The associated errors can be computed before performing simulations and it is shown that the errors happen especially in the cross correlation between distant points and that they are negligibly small in practical situations.