Deep learning has presented remarkable progress in various tasks. Despite the excellent
performance, deep learning models remain not robust, especially to well-designed …

Certifying the safety or robustness of neural networks against input uncertainties and
adversarial attacks is an emerging challenge in the area of safe machine learning and …

The objective of this research is to present the state of the art of the safety assurance of
Artificial Intelligence (AI)-based systems and guidelines on future correlated work. For this …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Recent developments in data science and machine learning have inspired a new wave of
research into data-driven modeling for mathematical optimization of process applications …

There has been an increasing interest in using neural networks in closed-loop control
systems to improve performance and reduce computational costs for on-line implementation …

Neural networks can be used as approximations of several complex control schemes such
as model predictive control. We show in this paper which properties deep neural networks …

The unprecedented advancement of artificial intelligence (AI) in recent years has altered our
perspectives on software engineering and systems engineering as a whole. Nowadays …

Deep equilibrium models are based on implicitly defined functional relations and have
shown competitive performance compared with the traditional deep networks. Monotone …

The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a
dynamical system perspective. The classical recursive equations describing the evolution of …