  In control design most control strategies are model-based and require
accurate models to be applied successfully. Due to simplifications and the
model-reality-gap physics-derived models frequently exhibit deviations from
real-world-systems. Likewise, purely data-driven methods often do not
generalise well enough and may violate physical laws. Recently Physics-Guided
Neural Networks (PGNN) and physics-inspired loss functions separately have
shown promising results to conquer these drawbacks. In this contribution we
extend existing methods towards the identification of non-autonomous systems
and propose a combined approach PGNN-L, which uses a PGNN and a
physics-inspired loss term (-L) to successfully identify the system's dynamics,
while maintaining the consistency with physical laws. The proposed method is
demonstrated on two real-world nonlinear systems and outperforms existing
techniques regarding complexity and reliability.
