Vorlesungen
Our teaching program addresses conceptual and technical foundations in depth and rigor in the areas of control theory, cyber-physics and machine learning. We target students from engineering and mathematics in attempting to deploy a systematic and conceptual operating thinking as well as a slice of our passion in the field of complex dynamical systems and decision making algorithms and foundations. Last but not least, we demonstrate in the classes, practical lab assignments and exercises their application to modern cross-disciplinary technologies and industries.
Regelungstheorie
Ziel der Vorlesung Regelungstheorie ist eine konzise Einführung in die Theorie der dynamischen Systeme und optimalen Steuerung. Dabei stehen im Vordergrund elementarste und wichtigste konzeptionelle und technische Ausführungen der Lyapunov-Stabilität, Steuerbarkeit und Berechnung von optimalen Steuerungen zeitkontinuierlicher Systeme.
Weiterlesen
Hybrid Dynamical Systems
The course gives an overview of the basic concepts of modeling and control methods in discrete-event systems and a variety of classes of hybrid dynamical systems involving abstraction and algebraic techniques. Rigorous mathematical foundations of automata theory, Petri nets, hybrid automata, mixed-logical dynamics, impulsive systems, switched systems along with applications in engineering are covered in the course.
Weiterlesen
Data-driven Control
In this course, we focus on a mix of established and emerging methods that are driving current developments in many directions of control theory. In particular, we will focus on the key challenges of discovering dynamics from data and finding data-driven representations that make nonlinear systems amenable to linear analysis. Vast numerical and programming demonstrations enrich a mathematically systematic presentation of ideas and techniques.
Dynamical Systems and Neural Networks
In this course students shall learn the fundamental principles governing complex systems via the theory of dynamical systems. We shall mainly rely on PDEs and ODEs as prototypical models to capture the dynamics of various systems from vehicle motion to the spreading of diseases. By the course's end, students will have gained a solid foundational understanding of dynamical systems, ODEs, PDEs and neural networks, equipped with analytical and computational tools to address interdisciplinary problems effectively.
Autonomous Systems
This course gives an overview of various modules and aspects of autonomous systems. The students would learn different concepts such as perception, object tracking, SLAM, planning and control, which are core components of many autonomous systems. Additionally, the students would have the opportunity to learn how to implement these theoretical concepts in practice, as part of code demos.
Mechatronik
Über die Gliederung auf mehreren Modulen werden verschiedene Aspekte der Mechatronik addressiert. Dabei gilt besonderes Augenmerk gängigen Werkzeugen gehöriger Module. Die Vorlesung richtet sich an Studierende des Maschinenbaus, Elektrotechnik und Informatik und entwickelt eine gemeinsame "Sprache" im Kontext der Systemtheorie.
Weiterlesen
Machine Learning
The course gives an overview of the basic and mordern concepts, mathematical techniques and algorithms of deep neural networks (DNN), including convolutional neutral networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), as well as reinforcement learning (RL). In addition to rigorous mathematical foundations, the covered methods are illustrated by implementations on applications in various domains, including environment perception, autonomous driving and cancer research.
Weiterlesen
Maschinendynamik
Es werden die Grundlagen der Modellbildung und Analyse von komplexen mechanischen Strukturen und Systemen vermittelt. Zentrale Rolle spielen die Euler-Lagrange- und Newton-Euler-Gleichungen. Studierende erfahren einen systematischen Aufbau von technischen Konzepten, die allgemeine mathematische Beschrebung von Mehrkörpersystemen inkl. spezielle lineare Systemklassen sowie Methoden zur Lösung bzw. Analyse von Bewegungsgleichungen in Zeit- und Frequenzbereich. Die entwickelten Konzepte und Methoden werden in praktischen Beispielen der Maschinen- und Systemdynamik demonstriert.
Weiterlesen