In this article, we investigate the approximation ability of recurrent neural networks (RNNs) with stochastic inputs in state space model form. More explicitly, we prove that open dynamical systems with stochastic inputs can be well-approximated by a speci ...
In this thesis, we study systems of active particles interacting via generic torques of different nature. We analyze the phase behavior of these systems, which results from the interplay between self-propulsion, excluded-volume and torques.
We tackle the p ...
The exploration of open quantum many-body systems -systems of microscopic size exhibiting quantum coherence and interacting with their surrounding- has emerged as a key research area over the last years. The recent advances in controlling and preserving qu ...
Trajectory planning through dynamical systems (DS) provides robust control for robots and has found numerous applications from locomotion to manipulation. However, to date, DS for controlling rhythmic patterns are distinct from DS used to control point to ...
Consider a linear elliptic PDE defined over a stochastic stochastic geometry a function of N random variables. In many application, quantify the uncertainty propagated to a quantity of interest (QoI) is an important problem. The random domain is split into ...
The extensive use of frequency-domain tools for analyzing and controlling linear systems have become indispensable for the control systems engineer. However, due to the increased performance demands on today's industrial systems, the effects of certain non ...
Transitional turbulence in shear flows is supported by a network of unstable exact invariant solutions of the Navier-Stokes equations. The network is interconnected by heteroclinic connections along which the turbulent trajectories evolve between invariant ...
The reconstruction of a diffusion field, such as temperature, from samples collected by a sensor network is a classical inverse problem and it is known to be ill-conditioned. Previous work considered source models, such as sparse sources, to regularize the ...
This paper presents a method for learning discrete robot motions from a set of demonstrations. We model a motion as a nonlinear autonomous (i.e. time-invariant) Dynamical System (DS), and define sufficient conditions to ensure global asymptotic stability a ...
Coupled neural networks, the Internet, World Wide Web, social networks and interacting biological networks are few examples of systems which consist of a large number of interacting dynamical units. Collective behavior of such systems is a consequence of t ...
