Date: Wed. May 15, 2024
Event: FAU MoD Lecture
Event type: On-site / Online
Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)
FAU MoD Lecture: Using system knowledge for improved sample efficiency in data-driven modeling and control of complex technical systems
Speaker: Prof. Dr. Sebastian Peitz
Affiliation: Universität Paderborn (Germany)
Abstract. Modern technical systems such as autonomous vehicles, the electric grid or nuclear fusion reactors are extremely complex, which requires powerful techniques for predicting or controlling their behavior. As in almost all areas of science as well as our daily lives, machine learning has had a huge impact on the area of modeling and control of technical systems in recent years. However, the complexity of these systems renders the learning very data-hungry. The aim of this talk is thus to discuss different approaches to leverage system knowledge – and in particular symmetries – such that we can significantly improve the sample efficiency. Our discussion ranges from learning the dynamics from data to reinforcement learning. We will emphasize the benefits of exploiting knowledge using various examples from fluid mechanics.
BIO.- Sebastian Peitz received his B.Sc. and M.Sc. degrees in mechanical engineering from RWTH Aachen University, Germany, in 2011 and 2013, respectively, and his Ph.D. in Mathematics from Paderborn University in 2017. He is currently assistant professor for “Data Science for Engineering” at the Department of Computer Science at Paderborn University. His research interests are multiobjective optimization, optimal control, and data-driven modeling and control of complex systems.
Recording/Video
AUDIENCE
This is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.
WHEN
Wed. May 15, 2024 at 14:00H (Berlin time)
WHERE
On-site / Online
[On-site] Friedrich-Alexander-Universität Erlangen-Nürnberg.Room H13 Johann-Radon-Hörsaal
Cauerstraße 11, 91058 Erlangen
GPS-Koord. Raum: 49.573764N, 11.030028E [Online] FAU Zoom link
Meeting ID: 624 1094 3213 | PIN code: 694096
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