The Chair of Mechatronics is characterized by a pioneering combination of physical understanding, mechatronic engineering and scientific machine learning. Our expertise lies in understanding, modelling and optimizing complex interactions between mechanical, electronic and other subsystems. We rely not only on classic methods of physical modeling, but also on innovative approaches from the field of scientific machine learning. Our aim is not only to research new methods and integrate them into the development and optimization of mechatronic systems, but also to increase the performance of these systems in real applications.
Methods developed at our chair aim to be usable for a wide range of applications and are applied to real industrial problems from the automotive, energy and medical sectors for validation.

Students and researchers at our department benefit from the unique opportunity to work in an interdisciplinary environment that combines traditional concepts of mechatronics with the latest developments in machine learning.

Our current range of student work can be found on github:
Student Thesis