Marco Forgione: Dynamical systems modelling with deep learning tools
30 March 2021 - 30 March 2021
Online - 11:30
In recent years, considerable research has been pursued at the interface between dynamical system theory and deep learning, leading to advances in both fields. In this talk, I will discuss two approaches for dynamical system modelling with deep learning tools and concepts that we are developing at IDSIA.
In the first approach, we adopt tailor-made state-space model structures where neural networks describe the most uncertain system components, while we retain structural/physical knowledge, if available. Specialised training algorithms for these model structures are also discussed. The second approach is based on a neural network architecture, called dynoNet, where linear dynamical operators parametrised as rational transfer functions are used as elementary building blocks. Owing to the rational transfer function parametrisation, these blocks can describe infinite impulse response (IIR) filtering operations. Thus, dynoNet may be seen as an extension of the 1D-CNN architecture, as the 1D-Convolutional units of 1D-CNNs correspond to the finite impulse response (FIR) filtering case.

The speaker

Marco Forgione received his PhD in Systems and Control Engineering from the Delft University of Technology (The Netherlands) in 2014. Before joining IDSIA, he was Postdoctoral researcher at the Ecole Centrale de Lyon (France) and R&D Control Engineering Consultant at Whirlpool EMEA (Italy). His research interests lie at the intersection of Control Theory, System Identification, Dynamical Systems, and Machine Learning.