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SUMMARY:Ivan Markovsky\, "SHORT Course: Numerical methods for direct data-
 driven systems theory\, signal processing\, and control"
DTSTART;VALUE=DATE-TIME:20260609T070000Z
DTEND;VALUE=DATE-TIME:20260612T083000Z
DTSTAMP;VALUE=DATE-TIME:20260609T101400Z
UID:indico-event-951@indico.gssi.it
DESCRIPTION:LecturerIvan Markovsky (ICREA\, Barcelona)Timetable and worklo
 adTuesday 9 MLH\, 9-10.30Wednesday 10 MLH\, 10.45 - 12.15\, 16-17.30Thursd
 ay 11 MLH\,  9-10.30Friday 12 MLH\,  9-10.30Course contentThe course pro
 vides an overview of\, and hands-on experience with\, newly emerged method
 s for the identification\, analysis\, and design of discrete-time linear t
 ime-invariant dynamical systems. The key idea is to view a dynamical syste
 m as a set of trajectories--the behavior--rather than equations--a represe
 ntation. This representation-free perspective on systems theory\, known as
  the behavioral approach\, is naturally suited for dealing with data\, tha
 t is\, trajectories of the system. Moreover\, it leads to novel\, simple\,
  and effective computational methods. At the core of this approach is a fi
 nite-time representation of the system by the image of a Hankel matrix con
 structed from the raw data. This data-driven\, nonparametric representatio
 n allows us to use basic linear algebraic operations and leads to numerica
 l methods for problems that are nontrivial to solve using classical transf
 er function and state-space techniques. Examples presented in the course i
 nclude missing data estimation\, fault detection\, and computing distance 
 to uncontrollability.References:- I. Markovsky. The Behavioral Toolbox. In
  Proc. of Machine Learning Research\, 242:130--141\, 2024. https://proceed
 ings.mlr.press/v242/markovsky24a/markovsky24a.pdf- I. Markovsky\, L. Huang
 \, and F. Dörfler. “Data-driven control based on behavioral approach: F
 rom theory to applications in power systems”. In: IEEE Control Systems M
 agazine\, 43:28--68\, 2023. https://imarkovs.github.io/tutorial.pdf- I. Ma
 rkovsky. Low-Rank Approximation: Algorithms\, Implementation\, Application
 s. Springer\, 2019. https://imarkovs.github.io/book/book2e-2x1.pdf\n\nhttp
 s://indico.gssi.it/event/951/
URL:https://indico.gssi.it/event/951/
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