Data-driven nonlinear modelling and control of structures

The aim of this research project is to develop a multi-level modeling approach for the identification and control of the nonlinear dynamic behavior of civil structures. Using active learning, artificial neural networks, and Gaussian process regression, vibration control systems such as dampers and dissipators will be efficiently modeled and controlled. This will enable structures to autonomously capture, analyze, and adapt their dynamic behavior to changing conditions.

The project is carried out as part of a tandem project in collaboration with researchers from the Institute of Automatic Control at RWTH Aachen University.