The overall objective of the FOR 3022 is to gain a thorough understanding of an integrated structural health monitoring (SHM) system in laminates with layers of large impedance difference using guided ultrasonic waves (GUW) under real-world conditions. In this subproject we focus on automated damage detection and merge the expertise from mathematics and computer science. As the basis for the automated methods serve mathematical models built upon physical principles, mathematical tools to make the models computationally tractable and machine learning methods. Consequently, WG Lorenz (working on physics-informed neural networks (PINNs)) and WG Gräßle (working on model order reduction and data assimilation) join WG Bosse (working on machine learning methods.