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

The causal connectivity of a network is often inferred to understand network function. It is
arguably acknowledged that the inferred causal connectivity relies on the causality measure …

We devise a machine learning technique to solve the general problem of inferring network
links that have time delays using only time series data of the network nodal states. This task …

Learning dynamical networks based on time series of nodal states is of significant interest in
systems science, computer science, and control engineering. Despite recent progress in …

The Granger causality (GC) analysis has been extensively applied to infer causal
interactions in dynamical systems arising from economy and finance, physics …

Neural networks (NNs) have been widely used to solve partial differential equations (PDEs)
in the applications of physics, biology, and engineering. One effective approach for solving …

Predicting network dynamics based on data, a problem with broad applications, has been
studied extensively in the past, but most existing approaches assume that the complete set …

It has been recognized that many complex dynamical systems in the real world require a
description in terms of multiplex networks, where a set of common, mutually connected …

Reconstruction of anatomical connectivity from measured dynamical activities of coupled
neurons is one of the fundamental issues in the understanding of structure-function …

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of
previous works, to fully reconstruct the network structure from observed binary data remains …