Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates

Neumann K, Lemme A, Steil JJ. Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates. Presented at the Int. Conference Intelligent Robotics and Systems, Tokio

Implications of a temperature-dependent magnetic anisotropy for superparamagnetic switching

The macroscopic magnetic moment of a superparamagnetic system has to overcome an energy barrier in order to switch its direction. This barrier is formed by magnetic anisotropies in the material and may be surmounted typically after 10^9 to 10^12 attempts per second by thermal fluctuations. In a first step, the associated switching rate may be described by a Neel-Brown-Arrhenius law, in which the energy barrier is assumed as constant or a given temperature. Yet, magnetic anisotropies in general depend on temperature themselves which is known to modify the Neel-Brown-Arrhenius law. We illustrate quantitatively the implications of a temperature-dependent anisotropy on the switching rate and in particular for the interpretation of the prefactor as an attempt frequency. In particular, we show that realistic numbers for the attempt frequency are obtained when the temperature dependence of the anisotropy is taken into account.Comment: 15 pages, 5 figure

Phonon-assisted transitions from quantum dot excitons to cavity photons

For a single semiconductor quantum dot embedded in a microcavity, we theoretically and experimentally investigate phonon-assisted transitions between excitons and the cavity mode. Within the framework of the independent boson model we find that such transitions can be very efficient, even for relatively large exciton-cavity detunings of several millielectron volts. Furthermore, we predict a strong detuning asymmetry for the exciton lifetime that vanishes for elevated lattice temperature. Our findings are corroborated by experiment, which turns out to be in good quantitative and qualitative agreement with theory

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project