The lecture focusses on the development of physics-informed neural networks for intelligent real-time modeling and simulation of temperature processes in additive manufacturing. The resulting digital twin can be used to efficiently predict component quality deficiencies and optimize 3D printing processes. In particular, cost and working time can be reduced.