Abstract Using Artificial Neural Networks (ANN) for nonlinear system identification has
proven to be a promising approach, but despite of all recent research efforts, many practical …

This manuscript details and extends the system identification methods leveraging the
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …

A known challenge when building nonlinear models from data is to limit the size of the
model in terms of the number of parameters. Especially for complex nonlinear systems …

The development of simple, low-order and accurate unsteady aerodynamic models
represents a crucial challenge for the design optimisation and control of fluid dynamical …

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

This article presents a survey of the use of artificial intelligence/machine learning (AI/ML)
techniques in navigation and tracking applications, with a focus on the dynamical models …

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 …

The nonlinear behavior of loudspeakers is of great interest in a number of audio processing
algorithms, as it may have a detrimental effect on their performance. These algorithms may …

Multivariate functions emerge naturally in a wide variety of data-driven models. Popular
choices are expressions in the form of basis expansions or neural networks. While highly …