The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

This paper discusses the recent progress in understanding the properties of transient
dynamics in complex ecological systems. Predicting long-term trends as well as sudden …

Visibility graph has established itself as a powerful tool for analyzing time series. We in this
paper develop a novel multiscale limited penetrable horizontal visibility graph (MLPHVG) …

Despite the development of large-scale data-acquisition techniques, experimental
observations of complex systems are often limited to a tiny fraction of the system under …

This article presents a review of computational methods for connectivity inference from
neural activity data derived from multi-electrode recordings or fluorescence imaging. We first …

Single perovskite alloy nanowire capable of emitting lasing broadly and continuously is
highly desirable for the miniaturization and integration of all-photonic devices. However, due …

Complex systems are extremely hard to predict due to its highly nonlinear interactions and
rich emergent properties. Thanks to the rapid development of network science, our …

In this article, we explore the state-space formulation of a network process to recover from
partial observations the network topology that drives its dynamics. To do so, we employ …

In applications of nonlinear and complex dynamical systems, a common situation is that the
system can be measured, but its structure and the detailed rules of dynamical evolution are …

Data-driven model discovery of complex dynamical systems is typically done using sparse
optimization, but it has a fundamental limitation: sparsity in that the underlying governing …