  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 for physical plausibility. To address this issue,
we propose a physics-guided hybrid approach for modeling non-autonomous systems
under control. Starting from a traditional physics-based model, this is
extended by a recurrent neural network and trained using a sophisticated
multi-objective strategy yielding physically plausible models. While purely
data-driven methods fail to produce satisfying results, experiments conducted
on real data reveal substantial accuracy improvements by our approach compared
to a physics-based model.
