Dario Piga: Deep learning for system identification, and viceversa
27 April 2023 - 27 April 2023
Room C1.02 - East Campus
The distinction between deep learning and system identification can be quite intricate, as these fields have evolved through decades of research and contributions from diverse communities. In this talk, we aim to showcase how concepts from deep learning and system identification can be synergistically combined to create innovative algorithms and tools for data-driven modeling and analysis of nonlinear dynamical systems. Three main results will be presented:
- A novel neural network architecture, called dynoNet, which integrates transfer functions into a deep learning framework, providing a bridge between traditional system identification and modern deep learning techniques.
- A new algorithm for rapid model adaptation of neural network models, enabling fast and efficient fine-tuning to accommodate changes in system dynamics or operating conditions.
- Quantification of predictive uncertainty in deep-learning models describing nonlinear dynamical systems, providing insights into model confidence and facilitating robust decision-making.

The speaker

 Dario Piga received his Ph.D. in Systems Engineering from the Politecnico di Torino (Italy) in 2012. He was Assistant Professor at the IMT School for Advanced Studies Lucca (Italy) and since 2017 he has been Senior Researcher at the IDSIA Dalle Molle Institute for Artificial Intelligence, USI/SUPSI, in Lugano (Switzerland). His main research interests include system identification, deep learning, and optimal control. He collaborates with international companies and coordinates several research projects for the development of innovative AI-based systems in the manufacturing, transportation, biomedical and chemical industry. He is member of the IEEE-CSS Conference Editorial Board and Associate Editor of the IFAC journal Automatica.

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