Welcome to the Professorship of Computer Science in Mechanical Engineering at the Institute of Automation Technology

As an interdisciplinary chair, we work at the interface of computer science and mechanical engineering. Our research problems and topics arise primarily from mechanical engineering and related disciplines such as plant engineering and automation, while our solutions are based in particular on machine learning algorithms and symbolic artificial intelligence. Our research addresses, among other things, algorithms for problems in areas such as physical simulation and data-driven modeling, monitoring (including anomaly detection and predictive maintenance), the control and diagnosis of complex technical systems, and the solution of discrete and hybrid planning problems. The safety requirements that technical systems must meet in operation directly affect the range of possible solutions, which must be interpretable and robust. Our research and solutions therefore deliberately combine data-driven machine learning approaches with domain-specific prior knowledge and symbolic algorithms in order to leverage the advantages of both. From a holistic perspective, this results in an overarching approach to robust artificial intelligence in the context of cyber-physical systems.

Further information about our academic expertise can be found on the page dedicated to our research focus areas.

Our ongoing and completed research projects, as well as our publications, provide deeper insight into our activities.

 

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