Since the early days of chaos theory [1,2] and complexity research [3], and the boom of results obtained since the 1980s, we have witnessed a growing body of work on the applications of nonlinear dynamics, chaos and complexity in diverse fields of science, e.g., in neuronal dynamics and brain research, or laser dynamics and excitable media in chemistry, or the dynamics of ecological populations and communities, to mention but a few. On the one hand, these applications fueled a revived interest in synchronization phenomena and control of networks. On the other hand, they inspired investigations of the effects of the coupling topology in networks where each node represents a subsystem with an intrinsic nonlinear dynamics; between the classical extremes of next neighbor coupling (diffusive) and the global all-to-all coupling (mean field) interesting new phenomena can be found such as, for instance, chimera states that have come into the focus of research recently. While chaos is often defined and observed in deterministic nonlinear dynamics, the importance of temporal fluctuations and noise is widely acknowledged and, in particular, the cooperative action of noise and nonlinearities can enrich the repertoire of dynamical phenomena and the emergence of complex structures.The European Physics Journal Special Topics (EPJST) edition at hand collects a series of articles reflecting recent advances in nonlinear dynamics and complex structures. Contributors to this issue are renowned specialists in a field belonging to nonlinear research and, in fact, some of the authors even established its foundations. While some contributions of this issue still tackle fundamental aspects of nonlinear dynamics, the majority of articles in this collection uses the arsenal of nonlinear methods to model questions belonging to a topical field. We tried to map this partition by dividing the present edition into sections.A first section on Fundamental Aspects of Nonlinear Dynamics is opened with a mini-review by Lai and Grebogi [4] whose main purpose is to argue that quasiperiodicity tends to suppress multistability, as does random noise. Through an analysis of a