  In this paper, we introduce a data-driven modeling approach for dynamics
problems with latent variables. The state-space of the proposed model includes
artificial latent variables, in addition to observed variables that can be
fitted to a given data set. We present a model framework where the stability of
the coupled dynamics can be easily enforced. The model is implemented by
recurrent cells and trained using backpropagation through time. Numerical
examples using benchmark tests from order reduction problems demonstrate the
stability of the model and the efficiency of the recurrent cell implementation.
As applications, two fluid-structure interaction problems are considered to
illustrate the accuracy and predictive capability of the model.
