Model-ordereductie gebaseerd op rationale interpolatie voor digitale tweelingen: Adaptive-Anderson-Antoulas voor tijdsdomein-data en niet-lineaire systemen

The main goal of this PhD project is the development of parametric model order reduction methods for the efficient evaluation of the models involved in the estimation process of Digital Twins for high-dynamic mechatronic systems. The desired reduced model will be able to undergo reconfigurations over the state and parameter space, in a trade-off between accuracy and computational load. The focus will be set on the transformation of expensive flexible multibody and nonlinear finite element models into lower cost parameterized counterparts which are sufficiently small and have an ordinary differential equation structure for effective inclusion in a single-step estimation framework.