The nonlinear autoregressive exogenous (NARX) model shows a strong expression
capacity for nonlinear systems since these systems have limited information about their …

There are many important fields involving the multilinear system identification. A great
number of parameters to be identified is an important challenge, leading to the need for …

Tensor decompositions can be a powerful tool when faced with the curse of dimensionality
and have been applied in myriad applications. Their application to problems in the control …

Slow convergence and low accuracy are two main drawbacks in nonlinear system
identification methods. It becomes more complicated when time delay and noises are …

In this paper we present ExSpliNet, an interpretable and expressive neural network model.
The model combines ideas of Kolmogorov neural networks, ensembles of probabilistic trees …

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for
system identification. Although DNNs show impressive approximation ability in various …

Continuous-time (CT) modeling has proven to provide improved sample efficiency and
interpretability in learning the dynamical behavior of physical systems compared to discrete …

A multi-physics high-fidelity computational model is required to study the melting and grain
growth phenomena in a laser powder-bed fusion (LPBF) additive manufacturing process …

In the context of kernel machines, polynomial and Fourier features are commonly used to
provide a nonlinear extension to linear models by mapping the data to a higher-dimensional …

In engineering practice, sampling frequencies much higher than the Nyquist frequency are
often used to obtain accurate measurements of the dynamic characteristics of nonlinear …