All beauty, richness and harmony in the emergent dynamics of a complex system largely
depend on the specific way in which its elementary components interact. The last twenty-five …

Artificial Intelligence (AI) has advanced material research that were previously intractable,
for example, the machine learning (ML) has been able to predict some unprecedented …

Real-world networks often consist of millions of heterogenous elements that interact at
multiple timescales and length scales. The fields of statistical physics and control theory both …

A reflection of our ultimate understanding of a complex system is our ability to control its
behavior. Typically, control has multiple prerequisites: it requires an accurate map of the …

Most networked systems of scientific interest are characterized by temporal links, meaning
the network's structure changes over time. Link temporality has been shown to hinder many …

This paper studies the problem of controlling complex networks, ie, the joint problem of
selecting a set of control nodes and of designing a control input to steer a network to a target …

The efficient control of complex dynamical systems has many applications in the natural and
applied sciences. In most real-world control problems, both control energy and cost …

Controllability and observability have long been recognized as fundamental structural
properties of dynamical systems, but have recently seen renewed interest in the context of …

Controlling complex networks is of paramount importance in science and engineering.
Despite the recent development of structural controllability theory, we continue to lack a …

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