530 Physik
Refine
Year of publication
- 2024 (11)
- 2023 (33)
- 2022 (190)
- 2021 (95)
- 2020 (183)
- 2019 (237)
- 2018 (238)
- 2017 (125)
- 2016 (36)
- 2015 (31)
- 2014 (26)
- 2013 (8)
- 2012 (18)
- 2011 (15)
- 2010 (13)
- 2009 (11)
- 2008 (7)
- 2007 (22)
- 2006 (49)
- 2005 (26)
- 2004 (30)
- 2003 (12)
- 2002 (8)
- 2001 (8)
- 2000 (4)
- 1999 (5)
- 1998 (6)
- 1997 (7)
- 1996 (7)
- 1995 (9)
- 1994 (8)
- 1989 (1)
- 1972 (1)
Document Type
- Article (886)
- Doctoral Thesis (362)
- Postprint (95)
- Other (49)
- Preprint (36)
- Habilitation Thesis (22)
- Review (12)
- Master's Thesis (10)
- Monograph/Edited Volume (4)
- Course Material (2)
Keywords
- diffusion (43)
- anomalous diffusion (31)
- gamma rays: general (20)
- synchronization (18)
- Synchronisation (16)
- organic solar cells (16)
- stochastic processes (14)
- ISM: supernova remnants (13)
- cosmic rays (13)
- data analysis (12)
Institute
- Institut für Physik und Astronomie (1480) (remove)
This thesis analyses synchronization phenomena occurring in large ensembles of interacting oscillatory units. In particular, the effects of nonisochronicity (frequency dependence on the oscillator's amplitude) on the macroscopic transition to synchronization are studied in detail. The new phenomena found (Anomalous Synchronization) are investigated in populations of oscillators as well as between oscillator's ensembles.
In the present work synchronization phenomena in complex dynamical systems exhibiting multiple time scales have been analyzed. Multiple time scales can be active in different manners. Three different systems have been analyzed with different methods from data analysis. The first system studied is a large heterogenous network of bursting neurons, that is a system with two predominant time scales, the fast firing of action potentials (spikes) and the burst of repetitive spikes followed by a quiescent phase. This system has been integrated numerically and analyzed with methods based on recurrence in phase space. An interesting result are the different transitions to synchrony found in the two distinct time scales. Moreover, an anomalous synchronization effect can be observed in the fast time scale, i.e. there is range of the coupling strength where desynchronization occurs. The second system analyzed, numerically as well as experimentally, is a pair of coupled CO₂ lasers in a chaotic bursting regime. This system is interesting due to its similarity with epidemic models. We explain the bursts by different time scales generated from unstable periodic orbits embedded in the chaotic attractor and perform a synchronization analysis of these different orbits utilizing the continuous wavelet transform. We find a diverse route to synchrony of these different observed time scales. The last system studied is a small network motif of limit cycle oscillators. Precisely, we have studied a hub motif, which serves as elementary building block for scale-free networks, a type of network found in many real world applications. These hubs are of special importance for communication and information transfer in complex networks. Here, a detailed study on the mechanism of synchronization in oscillatory networks with a broad frequency distribution has been carried out. In particular, we find a remote synchronization of nodes in the network which are not directly coupled. We also explain the responsible mechanism and its limitations and constraints. Further we derive an analytic expression for it and show that information transmission in pure phase oscillators, such as the Kuramoto type, is limited. In addition to the numerical and analytic analysis an experiment consisting of electrical circuits has been designed. The obtained results confirm the former findings.
Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far-reaching applications in many domains, including engineering and medicine. In this paper, we formulate the synchronization control in dynamical systems as an optimization problem and present a multi-objective genetic programming-based approach to infer optimal control functions that drive the system from a synchronized to a non-synchronized state and vice versa. The genetic programming-based controller allows learning optimal control functions in an interpretable symbolic form. The effectiveness of the proposed approach is demonstrated in controlling synchronization in coupled oscillator systems linked in networks of increasing order complexity, ranging from a simple coupled oscillator system to a hierarchical network of coupled oscillators. The results show that the proposed method can learn highly effective and interpretable control functions for such systems.
This work deals with the connection between two basic phenomena in Nonlinear Dynamics: synchronization of chaotic systems and recurrences in phase space. Synchronization takes place when two or more systems adapt (synchronize) some characteristic of their respective motions, due to an interaction between the systems or to a common external forcing. The appearence of synchronized dynamics in chaotic systems is rather universal but not trivial. In some sense, the possibility that two chaotic systems synchronize is counterintuitive: chaotic systems are characterized by the sensitivity ti different initial conditions. Hence, two identical chaotic systems starting at two slightly different initial conditions evolve in a different manner, and after a certain time, they become uncorrelated. Therefore, at a first glance, it does not seem to be plausible that two chaotic systems are able to synchronize. But as we will see later, synchronization of chaotic systems has been demonstrated. On one hand it is important to investigate the conditions under which synchronization of chaotic systems occurs, and on the other hand, to develop tests for the detection of synchronization. In this work, I have concentrated on the second task for the cases of phase synchronization (PS) and generalized synchronization (GS). Several measures have been proposed so far for the detection of PS and GS. However, difficulties arise with the detection of synchronization in systems subjected to rather large amounts of noise and/or instationarities, which are common when analyzing experimental data. The new measures proposed in the course of this thesis are rather robust with respect to these effects. They hence allow to be applied to data, which have evaded synchronization analysis so far. The proposed tests for synchronization in this work are based on the fundamental property of recurrences in phase space.
We have studied the bifurcations in a three-dimensional incompressible magnetofluid with periodic boundary conditions and an external forcing of the Arnold-Beltrami-Childress (ABC) type. Bifurcation-analysis techniques have been applied to explore the qualitative behavior of solution branches. Due to the symmetry of the forcing, the equations are equivariant with respect to a group of transformations isomorphic to the octahedral group, and we have paid special attention to symmetry-breaking effects. As the Reynolds number is increased, the primary nonmagnetic steady state, the ABC flow, loses its stability to a periodic magnetic state, showing the appearance of a generic dynamo effect; the critical value of the Reynolds number for the instability of the ABC flow is decreased compared to the purely hydrodynamic case. The bifurcating magnetic branch in turn is subject to secondary, symmetry-breaking bifurcations. We have traced periodic and quasi- periodic branches until they end up in chaotic states. In particular detail we have analyzed the subgroup symmetries of the bifurcating periodic branches, which are closely related to the spatial structure of the magnetic field.
In the present work, we use symbolic regression for automated modeling of dynamical systems. Symbolic regression is a powerful and general method suitable for data-driven identification of mathematical expressions. In particular, the structure and parameters of those expressions are identified simultaneously.
We consider two main variants of symbolic regression: sparse regression-based and genetic programming-based symbolic regression. Both are applied to identification, prediction and control of dynamical systems.
We introduce a new methodology for the data-driven identification of nonlinear dynamics for systems undergoing abrupt changes. Building on a sparse regression algorithm derived earlier, the model after the change is defined as a minimum update with respect to a reference model of the system identified prior to the change. The technique is successfully exemplified on the chaotic Lorenz system and the van der Pol oscillator. Issues such as computational complexity, robustness against noise and requirements with respect to data volume are investigated.
We show how symbolic regression can be used for time series prediction. Again, issues such as robustness against noise and convergence rate are investigated us- ing the harmonic oscillator as a toy problem. In combination with embedding, we demonstrate the prediction of a propagating front in coupled FitzHugh-Nagumo oscillators. Additionally, we show how we can enhance numerical weather predictions to commercially forecast power production of green energy power plants.
We employ symbolic regression for synchronization control in coupled van der Pol oscillators. Different coupling topologies are investigated. We address issues such as plausibility and stability of the control laws found. The toolkit has been made open source and is used in turbulence control applications.
Genetic programming based symbolic regression is very versatile and can be adapted to many optimization problems. The heuristic-based algorithm allows for cost efficient optimization of complex tasks.
We emphasize the ability of symbolic regression to yield white-box models. In contrast to black-box models, such models are accessible and interpretable which allows the usage of established tool chains.
Switch It Inside-Out: "Schizophrenic" Behavior of All Thermoresponsive UCST-LCST Diblock Copolymers
(2019)
This feature article reviews our recent advancements on the synthesis, phase behavior, and micellar structures of diblock copolymers consisting of oppositely thermoresponsive blocks in aqueous environments. These copolymers combine a nonionic block, which shows lower critical solution temperature (LCST) behavior, with a zwitterionic block that exhibits an upper critical solution temperature (UCST). The transition temperature of the latter class of polymers is strongly controlled by its molar mass and by the salt concentration, in contrast to the rather invariant transition of nonionic polymers with type II LCST behavior such as poly(N-isopropylacrylamide) or poly(N-isopropyl methacrylamide). This allows for implementing the sequence of the UCST and LCST transitions of the polymers at will by adjusting either molecular or, alternatively, physical parameters. Depending on the location of the transition temperatures of both blocks, different switching scenarios are realized from micelles to inverse micelles, namely via the molecularly dissolved state, the aggregated state, or directly. In addition to studies of (semi)dilute aqueous solutions, highly concentrated systems have also been explored, namely water-swollen thin films. Concerning applications, we discuss the possible use of the diblock copolymers as "smart" nanocarriers.
The ratio of the proton ring velocity (VR) to the local Alfven speed (VA), in addition to proton ring distributions, plays a key factor in the excitation of magnetosonic waves at frequencies between the proton cyclotron frequency fcp and the lower hybrid resonance frequency fLHR in the Earth's magnetosphere. Here we investigate whether there is a statistically significant relationship between occurrences of proton rings and magnetosonic waves both outside and inside the plasmapause using particle and wave data from Van Allen Probe-A during the time period of October 2012 to December 2015. We also perform a statistical survey of the ratio of the ring energy (ER, corresponding to VR) to the Alfven energy (EA, corresponding to VA) to determine the favorable conditions under which magnetosonic waves in each of two frequency bands (fcp < f ≤ 0.5 fLHR and 0.5 fLHR < f < fLHR) can be excited. The results show that the magnetosonic waves in both frequency bands occur around the postnoon (12–18 magnetic local time, MLT) sector outside the plasmapause when ER is comparable to or lower than EA, and those in lower-frequency bands (fcp < f ≤ 0.5 fLHR) occur around the postnoon sector inside the plasmapause when ER/EA > ~9. However, there is one discrepancy between occurrences of proton rings and magnetosonic waves in low-frequency bands around the prenoon sector (6–12 MLT) outside the plasmapause, which suggests either that the waves may have propagated during active time from the postnoon sector after being excited during quiet time, or they may have locally excited in the prenoon sector during active time.
Tailoring the secondary surface morphology of electro-spun nanofibers has been highly desired, as such delicate structures equip nanofibers with distinct functions. Here, we report a simple strategy to directly reconstruct the surface of polyvinyl alcohol/polyvinylpyrrolidone (PVA/PVP) nanofibers by water evaporation. The roughness and diameter of the nanofibers depend on the temperature during vacuum drying. Surface changes of the nanofibers from smooth to rough were observed at 55 degrees C, with a significant drop in nanofiber diameter. We attribute the formation of the secondary surface morphology to the intermolecular forces in the water vapor, including capillary and the compression forces, on the basis of the results from the Fourier-transform infrared (FTIR) and X-ray photoelectron (XPS) spectroscopy. The strategy is universally effective for various electro-spun polymer nanofibers, thus opening up avenues toward more detailed and sophisticated structure design and implementation for nanofibers.