The simulation of complex multi-physical processes as additive manufacturing is extremely demanding and time-consuming using conventional numerical methods. Therefore, in engineering often only simplified analytical or empirical models are used. Thanks to advances in machine learning, reliable empirical approaches can increasingly be obtained from big data. However, the generation of big data often requires complex and expensive sensor technology. In the presence of high speed and large thermal gradients it is sometimes simply not possible to reliably generate certain data, even with the best sensor technology at hand.
In our group we investigate to what extent simulation with neural networks on the one hand and data-based empirical modeling on the other hand can be combined in a symbiotic manner. The ultimate goal is the generation of reliable models for complex dynamical systems known as digital twins.
|10.2022-09.2025||Monitoring data driven life cycle management with AR based on adaptive, AI-supported corrosion prediction for reinforced concrete structures under combined impacts. Subproject of DFG SPP 2388 – Hundred plus - Extending the Lifetime of Complex Engineering Structures through Intelligent Digitalization|
|03.2022–09.2024||GRK 2075: Modelling the constitutional evolution of building materials and structures with respect to aging|
|01.2022–12.2025||A participatory approach to cross-disciplinary teaching of data-centric methodological and application skills in higher education - KI4All. Acquired in the BMBF funding guideline Artificial Intelligence in Higher Education. Project lead of the 5 Mio € collaborative project with TU Clausthal und Ostfalia Hochschule is Prof. Dr.-Ing. Henning Wessels|