Speaker
Description
In order to achieve realistic simulations of complex dynamics, a detailed mathematical model and a precise numerical method are required. However, there are often inaccuracies or terms in the model that are uncertain or unknown. This is where data assimilation comes in, which corrects the model with the help of measurement data. Since data assimilation typically involves numerous simulations of the underlying system, model order reduction is employed to mitigate computational complexity. In this talk, we investigate model order reduction and data assimilation techniques for the inverse problem to infer unknown model parameters in order to identify possible damages in layered materials. This research is part of the DFG project FOR3022 “Ultrasonic Monitoring of Fibre Metal Laminates Using Integrated Sensors“.
