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Winter Term 2016/17, Doctoral School Events
2016-11-11 Doctoral School Seminar (Inst. Mathematik, Heinrichstr. 36, SR 11.34, 14:00—16:30, KFU)
A. Zubkova (KFU, advisor V. Kovtuneko): Introduction to the unfolding technique [show abstract]
J. McMahon (KFU, advisor K. Baur): Higher Frieze Patterns [show abstract]
L. Andritsch (KFU, advisor K. Baur): Boundary algebra of a GL2-dimer [show abstract]
2016-12-16 Doctoral School Seminar (Seminarraum 2 des Instituts für Geometrie, Kopernikusgasse 24, 10:00 (coffee break) 10:30—11:30, TU)
Daniel Ganellari (KFU, advisor G. Haase): Fast many-core solvers for the Eikonal equations in cardiovascular simulations [show abstract]
Stefan Planitzer (TU, advisor C. Elsholtz): Sequences with Property P [show abstract]
2017-01-20 Doctoral School Seminar (Inst. Mathematik, Heinrichstr. 36, SR 11.34, 14:00—16:30, KFU)
Stefan Lendl (TU, advisor B. Klinz): Combinatorial Optimization Problems with Interaction Costs [show abstract]
Sarah-Lena Bonkhoff (TU, advisor O. Steinbach): Space-Time Boundary Integral Equations of the Time Fractional Diffusion Equation

Abstract: In this talk we consider initial boundary value problems of the time fractional diffusion equation in a space-time cylinder with a time derivative of order α ∈ (0,1). This fractional differential equation arises in mathematical modeling of anomalous sub-diffusion processes, where the time fractional derivative replaces the first order time derivative of the standard diffusion equation. For this purpose we investigate two definitions of fractional derivatives, the Riemann-Liouville definition and the Caputo one.
As a starting point of the numerical investigation we construct a fundamental solution of the time fractional diffusion equation by means of the Fox H-functions and represent the solution in terms of layer potentials. With this approach we derive appropriate boundary integral equations and investigate the behaviour of the layer potentials in anisotroptic Sobolev spaces.[hide abstract]

Sarah Karasek (TU, advisor H. Friedl): Mixture Models for a Statistical Grayscale Image Analysis [show abstract]