  The prediction of system responses for a given fatigue test bench drive
signal is a challenging task, for which linear frequency response function
models are commonly used. To account for non-linear phenomena, a novel hybrid
model is suggested, which augments existing approaches using Long Short-Term
Memory networks. Additional virtual sensing applications of this method are
demonstrated. The approach is tested using non-linear experimental data from a
servo-hydraulic test rig and this dataset is made publicly available. A variety
of metrics in time and frequency domains, as well as fatigue strength under
variable amplitudes, are employed in the evaluation.
