10 citations · 29 across the 8 of their papers we have counts for
3 papers · 1 filter
Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems
Oliver Schön, Ricarda-Samantha Götte, Julia Timmermann
While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard…
Learning Data-Driven PCHD Models for Control Engineering Applications
Annika Junker, Julia Timmermann, Ansgar Trächtler
The design of control engineering applications usually requires a model that accurately represents the dynamics of the real system. In addition to classical physical modeling, powe…
Data-Driven Models for Control Engineering Applications Using the Koopman Operator
Annika Junker, Julia Timmermann, Ansgar Trächtler
Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the…