This survey presents recent research on determining control-theoretic properties and
designing controllers with rigorous guarantees using semidefinite programming and for …

The fundamental lemma by Jan C. Willems and co-workers is deeply rooted in behavioral
systems theory and it has become one of the supporting pillars of the recent progress on …

Neural operators map multiple functions to different functions, possibly in different spaces,
unlike standard neural networks. Hence, neural operators allow the solution of parametric …

We present a method to design a state-feedback controller ensuring exponential stability for
nonlinear systems using only measurement data. Our approach relies on Koopman-operator …

From logical reasoning to mental simulation, biological and artificial neural systems possess
an incredible capacity for computation. Such neural computers offer a fundamentally novel …

In this study, we present\textit {meta-sequential prediction}(MSP), an unsupervised
framework to learn the symmetry from the time sequence of length at least three. Our method …

In recent studies, deep reinforcement learning (RL) methods have been used for the real‐
time control of urban drainage systems (UDSs). However, the training process of an RL …

Many machine learning approaches for decision making, such as reinforcement learning,
rely on simulators or predictive models to forecast the time-evolution of quantities of interest …

Characterizing and quantifying microstructure evolution is critical to forming quantitative
relationships between material processing conditions, resulting microstructure, and …

The extended Dynamic Mode Decomposition (eDMD) is a very popular method to obtain
data-driven surrogate models for nonlinear (control) systems governed by ordinary and …