  In several studies, hybrid neural networks have proven to be more robust
against noisy input data compared to plain data driven neural networks. We
consider the task of estimating parameters of a mechanical vehicle model based
on acceleration profiles. We introduce a convolutional neural network
architecture that is capable to predict the parameters for a family of vehicle
models that differ in the unknown parameters. We introduce a convolutional
neural network architecture that given sequential data predicts the parameters
of the underlying data's dynamics. This network is trained with two objective
functions. The first one constitutes a more naive approach that assumes that
the true parameters are known. The second objective incorporates the knowledge
of the underlying dynamics and is therefore considered as hybrid approach. We
show that in terms of robustness, the latter outperforms the first objective on
noisy input data.
