The aim of this project is to further develop and analyze deep neural networks for industrial problems that allow existing domain knowledge to be incorporated into the architecture of the networks. Such a hybrid approach can capitalize on the complementary respective strengths of end-to-end learning approaches and "a priori models/rules". This approach promises substantially more efficient solutions for many fields of application. For example, significantly less data is required or the predictions of the ML model are consistent with existing knowledge.