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ABSTRACT / In this talk, I will present my previous research on inverse stochastic resonance as well as current research on physical networks. The first line of research concerns inverse stochastic resonance, a phenomenon where the frequency of noise-perturbed oscillations becomes minimal at an optimal noise intensity. By considering the influence of noise on a paradigmatic model of two units with excitable or oscillatory local dynamics, we have identified two generic scenarios for the onset of the effect. Concerning the second topic, we depart from the standard framework of network theory where networks are abstract representations of complex systems in which any physicality of their constituents is disregarded. Instead, we consider physical networks where nodes and links are non-intersecting physical objects embedded in space. We propose a random growth model for linear physical networks, where the links are straight cylinders that cannot overlap. With the increase of link thickness, the model exhibits different levels of physicality. First, in the weakly physical regime, the network becomes non-transparent, despite it's volume being zero-measure. In the strongly physical regime, it occupies a finite fraction of available space.
BIO / Iva Bačić completed her BSc, MSc and PhD at the Faculty of Physics, University of Belgrade, Serbia. She carried out the research within her PhD studies at the Institute of Physics Belgrade in Serbia, specializing in nonlinear dynamics, having defended her PhD titled "Self-organization in coupled excitable systems: the interplay between multiple timescale dynamics and noise". She is the co-author of six peer-reviewed publications. Since October 2020, she is a postdoctoral researcher at the Department of Network and Data Science at CEU.