Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent
years and are considered by both academia and industry to be key enablers for Industry 4.0 …

Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …

The utilization of surrogate models to approximate complex systems has recently gained
increased popularity. Because of their capability to deal with black-box problems and lower …

Due to the advances in the digitalization process of the manufacturing industry and the
resulting available data, there is tremendous progress and large interest in integrating …

In this paper, we describe the combination of machine learning and simulation towards a
hybrid modelling approach. Such a combination of data-based and knowledge-based …

The large-scale adoption of composite materials in industry has allowed for a greater
freedom in design and function of structures and their respective components. However, the …

Over the last 20 years Machine Learning (ML) has been applied to a wide variety of
applications in the fields of engineering and computer science. In the field of material …

Smart products supported by new step-changing technologies, such as Internet of Things
and artificial intelligence, are now emerging in the market. Smart products are cyber physical …

The worldwide energy demands and resource consumption are rising despite the efforts for
energy saving and emission reduction. This results from the combination of the supply chain …

Manufacturing of fiber reinforced polymer matrix composite materials is being done with
various methods in recent days. But controlling the accuracy of manufacturing and begetting …