Technical University of Berlin

We will study the question of whether the adjoint of a given matrix can be written as a rational function in the matrix. After showing necessary and sufficient conditions, rational interpolation theory will help to characterize the most important existing cases. Several topics related to our question will be explored. They range from short recurrence Krylov subspace methods to the roots of harmonic polynomials and harmonic rational functions. The latter have recently found interesting applications in astrophysics, which will briefly be discussed as well.

Many continuum models have been derived in recent years which describe the self-assembly of industrially utilisable crystalline films to a level of detail that allows qualitative comparisons with experiments. For thin-film problems, where the characteristic length scales in vertical and horizontal directions differ significantly, the governing surface diffusion equations can be reduced to simpler PDEs by making use of asymptotic expansions. Many mathematical problems and solutions emerge from such new evolution equations and many of them remind of Cahn-Hilliard type equations. The surface diffusion models are of high, of fourth or even sixth, order.

We present the modeling, model reduction and simulation results for heteroepitaxial growth as for Ge/Si quantum dot self-assembly. The numerical methods we are using are based on trigonometric interpolation. These kind of pseudospectral methods seem very well suited for simulating the coarsening of large quantum dot arrays. When the anisotropy of the growing crystalline film is strong, it might become necessary to add a corner regularisation to the model. Then the transition region between neighboring facets is still smooth, but its scale is rather small. In this case it might be useful to think about an adaptive extension of the existing method.

Figure 1: Ostwald ripening process of quantum dots depicted at consecutive time points. One fourth of the whole, periodic, simulated domain is shown.

Joint work with Peter Evans and Barbara Wagner

ABSTRACT "We give a short introduction to randomwalk in random environment

(RWRE) and some open problems connected to RWRE.

Then, in dimension larger than or equal to four we studyballisticity conditions and their interrelations. For this purpose, we dealwith a certain class of ballisticity conditions introduced by Sznitman anddenoted $(T)_\gamma.$ It is known that they imply a ballistic behaviour of theRWRE and are equivalent for parameters $\gamma \in (\gamma_d, 1),$ where$\gamma_d$ is a constant depending on the dimension and taking values in theinterval $(0.366, 0.388).$ The conditions $(T)_\gamma$ are tightly interwovenwith quenched exit estimates.

As a first main result we show that the conditions are infact equivalent for all parameters $\gamma \in (0,1).$ As a second main result,we prove a conjecture by Sznitman concerning quenched exit estimates.

Both results are based on techniques developed in a paperon slowdowns of RWRE by Noam Berger.

(joint work with Alejandro Ram\'{i}rez)"

This talk first introduces generalized Young measures (or DiPerna/Majda measures) in an $L^1$-setting. This extension to classical Young measures is able to quantitatively account for both oscillation and concentration phenomena in generating sequences.

We establish several fundamental properties like compactness and representation of nonlinear integral functionals and present some examples. Then, generalized Young measures generated by $W^{1,1}$- and $BV$-gradients are more closely examined and several tools to manipulate them (including averaging and approximation) are presented.

Finally, we address the question of characterizing the set of generalized Young measures generated by gradients in the spirit of the Kinderlehrer-Pedregal Theorem.

This is joint work with Jan Kristensen.

We discuss general and structured matrix polynomials which may be singular and may have eigenvalues at infinity. We discuss several real industrial applications ranging from acoustic field computations to optimal control problems.

We discuss linearization and first order formulations and their relationship to the corresponding techniques used in the treatment of systems of higher order differential equations.

In order to deal with structure preservation, we derive condensed/canonical forms that allow (partial) deflation of critical eigenvalues and the singular structure of the matrix polynomial. The remaining reduced order staircase form leads to new types of linearizations which determine the finite eigenvalues and corresponding eigenvectors.

Based on these new linearization techniques we discuss new structure preserving eigenvalue methods and present several real world numerical examples.