Scientists have developed hundreds of techniques to measure the interactions between
pairs of processes in complex systems, but these computational methods—from …

Causality detection methods based on mutual cross map** have been fruitfully developed
and applied to data originating from nonlinear dynamical systems, where the causes and …

Hydrological models with conceptual tip** bucket and process-based evapotranspiration
formulations are the most common tools in hydrology. However, these models consistently …

The ever-increasing complexity of modern power grids makes them vulnerable to cyber
and/or physical attacks. To protect them, accurate attack detection is essential. A challenging …

Understanding the coupling structure of interacting systems is an important open problem,
and many methods have been proposed to reconstruct a network from observed data. Most …

Wiring patterns of brain networks embody a trade-off between information transmission,
geometric constraints, and metabolic cost, all of which must be balanced to meet functional …

Reservoir computing is a machine learning framework that has been shown to be able to
replicate the chaotic attractor, including the fractal dimension and the entire Lyapunov …

Stereotyped oscillations in population neural activity recordings from immobilized
Caenorhabditis elegans have garnered interest for their striking low dimensionality and their …

Inferring network structures from available data has attracted much interest in network
science; however, in many realistic networks, only some of the nodes are perceptible while …

The properties of complex networked systems arise from the interplay between the dynamics
of their elements and the underlying topology. Thus, to understand their behavior, it is crucial …