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 interplay of mathematical modelling with experiments is one of the central elements in
systems biology. The aim of reverse engineering is to infer, analyse and understand …

Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling
and control efforts, providing a tremendous opportunity to extend the reach of model …

Inferring the structure and dynamics of network models is critical to understanding the
functionality and control of complex systems, such as metabolic and regulatory biological …

Cyber-physical systems embed software into the physical world. They appear in a wide
range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber …

The inner workings of a biological cell or a chemical reactor can be rationalized by the
network of reactions, whose structure reveals the most important functional mechanisms. For …

This technical note considers the identification of nonlinear discrete-time systems with
additive process noise but without measurement noise. In particular, we propose a method …

Stochastic differential equations (SDEs) are mathematical models that are widely used to
describe complex processes or phenomena perturbed by random noise from different …

Nowadays, industrial robots have been widely used in manufacturing, healthcare,
packaging, and more. Choosing robots in these applications mainly attributes to their …

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 …