Seminars

Abstract:

Abstract: The recent quick rise of generative AI has opened up many discussions on its use in science. In this interactive seminar we will explore this topic further. What is generative AI? When is it used? What are the benefits of generative AI? Is it useful at all? We will tackle these difficult questions together with you!

Distributed implementations of model predictive control (MPC) have received significant attention to address the challenges of large-scale, geographically-dispersed systems, where solving centralized optimizations is not viable or practical for real-time control. The essence of distributed MPC (DMPC) is to tackle the overall control problem by using multiple interacting MPC agents. This distribution has the ability to provide increased scalability, efficiency, and adaptability. The seminar will discuss the application of DMPC in thermal solar parabolic trough plants, which are based on concentrating the sunlight on a receiver to raise the temperature of a heat transfer fluid (HTF). Particularly, we will present DMPC methods to optimize the HTF flow rates that are pumped to different areas of the solar field. In this framework, we will place emphasis on approaches that use varying cooperation scenarios among sets of distributed agents.

Abstract: Mathematical models are crucial in many scientific fields. Modelling that starts from first principles can then be a good start. For operational use in real practice, however, the model should be in good accordance with experimental data. Nowadays, data-driven modelling becomes more and more popular, thus using a limited amount of prior knowledge.

The exhaust of particles and heat is one of the unresolved challenges on the way to a future fusion reactor. The main exhaust concept explored in the current fusion experiments is based on the modification of the magnetic edge topology by diverting the outermost field lines to dedicated target areas physically distant from the core plasma.