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Klimka Szwaykowska

Publications and source records attributed to Klimka Szwaykowska.

2 recordsLinked to original sources

Asymmetric noise-induced large fluctuations in coupled systems

Networks of interacting, communicating subsystems are common in many fields, from ecology, biology, epidemiology to engineering and robotics. In the presence of noise and uncertainty, inter- actions between the individual components can lead to unexpected complex system-wide behaviors. In this paper, we consider a generic model of two weakly coupled dynamical systems, and show how noise in one part of the system is transmitted through the coupling interface. Working synergistically with the coupling, the noise on one system drives a large fluctuation in the other, even when there is no noise in the second system. Moreover, the large fluctuation happens while the first system exhibits only small random oscillations. Uncertainty effects are quantified by showing how characteristic time scales of noise induced switching scale as a function of the coupling between the two coupled parts of the experiment. In addition, our results show that the probability of switching in the noise-free system scales inversely as the square of reduced noise intensity amplitude, rendering the virtual probability of switching to be an extremely rare event. Our results showing the interplay between transmitted noise and coupling are also confirmed through simulations, which agree quite well with analytic theory.

cond-mat.stat-mech↗

Pattern-Acquisition in Finite, Heterogenous, Delay-Coupled Swarms

The self-organizing behavior of swarms of inter- acting particles or agents is a topic of intense research in fields extending from biology to physics and robotics. In this paper, we carry out a systematic study of how the stable spatio-temporal patterns of a swarm depend on the number of agents, in the presence of time-delayed interaction and agent heterogeneity. We show how a coherent pattern is modified as the number of agents varies, including the time required to converge to it starting from random initial conditions. We discuss the implications of our results for curve-tracking using autonomous robotic systems.

nlin.PS↗