Extreme events gain the attention of researchers due to their utmost importance in various
contexts ranging from climate to brain. An observable that deviates significantly from its long …

The ability of machine learning (ML) models to “extrapolate” to situations outside of the
range spanned by their training data is crucial for predicting the long-term behavior of non …

Interactions amongst agents frequently exist only at particular moments in time, depending
on their closeness in space and movement parameters. Here we propose a minimal model …

Localization of hidden attractors is one of the most challenging tasks in the nonlinear
dynamics due to deficiency of properly justified analytical and numerical procedures. But …

In this study, we employ a machine learning approach to infer the complex dynamics of
dragon king extreme events. Specifically, we utilize two distinct machine learning …

We report intermittent large spiking events in a heterogeneous network of forced Josephson
junctions under the influence of repulsive interaction. The response of the individual …

Understanding and predicting uncertain things are the central themes of scientific evolution.
Human beings revolve around these fears of uncertainties concerning various aspects like a …

The remarkable flexibility and adaptability of both deep learning models and ensemble
methods have led to the proliferation for their application in understanding many physical …

The role of topological heterogeneity in the origin of extreme events in a network is
investigated here. The dynamics of the oscillators associated with the nodes are assumed to …

We study the dynamics of a parametrically and externally driven Rayleigh-Liénard hybrid
model and report the emergence of extreme bursting events due to a novel pulse-shaped …