Team

Lectures in structural analysis are core subjects in the civil engineering curriculum. As part of the B.Sc. programme, students are taught the fundamentals of modelling and analytical calculation methods for basic structural systems. This knowledge is supplemented and deepened in the M.Sc. programme with regard to plane structures, model abstraction and numerical calculation methods. The aim is to equip students with the necessary skills to find creative solutions to a wide range of research and engineering problems.

In the M.Sc. programme, the lecture portfolio is complemented by the content of “Scientific Machine Learning”. The focus here is on teaching complementary knowledge and methodological skills from the fields of Artificial Intelligence, Machine Learning and Deep Learning, with particular reference to civil engineering and computer-aided processes.

My research focuses on how we design, build, operate and recycle the built environment. In particular, I focus on machine and deep learning methods to transform the current “computer-aided” (CA-X) approach into an “artificial intelligence-assisted” (AIA-X) approach. In my view, these modern artificial intelligence algorithms allow an ideal combination of the technical expertise of civil engineers with the computational power of modern computers. The aim is to create a more human-centred, sustainable and aesthetic, yet reliable built environment.

Current topics include physics-informed deep learning, implicit geometry representation, data based material modelling of glass, polymers and concrete, reliability concepts, as well as computational and design approaches for new construction and manufacturing methods.