Data-driven Model Learning in Interconnected Systems
Seminarie met Paul Van den Hof (TU Eindhoven)
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- Inschrijvingen: tot 05 mei 2022
- Prijs: gratis
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Many of our current technological systems are composed of subsystems that have local dynamic behaviour and that interact with each other in a network structure. Typical examples are found in e.g., power systems (smart grids), biological systems, flexible mechanical structures, robotic networks, transportation systems and distribution networks.
For control, optimization and diagnostics of those systems, the interconnected nature leads to distributed and/or multi-agent type of approaches, while data is playing a paramount role in modelling, monitoring and controlling the dynamic behaviour of the system.
In this seminar we will present the emerging challenge of developing effective and scalable tools for data-driven dynamic model learning in large-scale interconnected systems, also referred to as dynamic networks. Focusing on linear dynamic networks, the problems of conceptual network modelling and local module learning will be presented, while graph-based tools are shown to be able to address problems like sensor and excitation/actuator allocation for accurately learning either a single module or a full network.
Lesgever/spreker
Paul Van den Hof is Full Professor of the Control Systems (CS) Group at the Department of Electrical Engineering of Eindhoven University of Technology. He is interested in data-driven modeling, control and optimization of dynamic systems in several technological fields: industrial process control, oil reservoir engineering, high-tech mechatronic and cyber-physical systems, etc. His focus is the development of fundamental techniques, such as data-driven modeling, closed-loop and control-oriented identification and data analytics, experimental design and performance monitoring, and model-based control, monitoring and optimization. Van den Hof often collaborates closely with international academic and industrial partners. Van den Hof was General Chair of the 13th IFAC Symposium on System Identification in 2003. He has served as member of the IFAC Council (1999-2005, 2017-2020), Associate Editor and Editor of Automatica (1992-2005), and Vice-President of IFAC (2017-2020). He is a Fellow of IFAC and of IEEE, and Honorary Member of the Hungarian Academy of Sciences. He holds an ERC Advanced Research Grant on Dynamic Network Identification.
AI for Time Series Seminars
Verscheidene onderzoeksgroepen in het Vlaams AI-onderzoeksprogramma verrichten onderzoek van wereldklasse in verband met tijdreeksen, zowel voor de ontwikkeling van algoritmes en tools, als voor een brede reeks toepassingen. In een recente rondvraag bij de Vlaamse AI-gemeenschap, bleek dat het onderwerp ’tijdreeksen’ het meest gevraagd werd om toekomstige workshops en cursussen over te organiseren. Met deze seminariereeks komen we aan die vraag tegemoet en brengen we onderzoekers die geïnteresseerd zijn in, of onderzoek verrichten naar, tijdreeksen samen. We bieden hen en andere belangstellenden een gevarieerd programma met nationale en internationale sprekers.
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