The Linear Parameter-Varying (LPV) framework provides a modeling and control design
toolchain to address nonlinear (NL) system behavior via linear surrogate models. Despite …

Machine Learning (ML) and linear System Identification (SI) have been historically
developed independently. In this paper, we leverage well-established ML tools—especially …

Available methods for identification of stochastic dynamical systems from input–output data
generally impose restricting structural assumptions on either the noise structure in the data …

In-context system identification aims at constructing meta-models to describe classes of
systems, differently from traditional approaches that model single systems. This paradigm …

Is it possible to understand the intricacies of a dynamical system not solely from its
input/output pattern, but also by observing the behavior of other systems within the same …

Controlling large-scale cyber-physical systems necessitates optimal distributed policies,
relying solely on local real-time data and limited communication with neighboring agents …

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of
data-driven modeling with the interpretability and generalization of underlying physical …

Artificial neural networks (ANN) have proven to be effective in dealing with the identification
nonlinear models for highly complex systems. To still make use of the prior information …

Artificial neural networks (ANN) have been shown to be flexible and effective function
estimators for the identification of nonlinear state-space models. However, if the resulting …

Neural network-based nonlinear system identification is crucial for various multi-step ahead
prediction tasks, including model predictive control and digital twins. These applications …