  This article is concerned with learning and stochastic control in physical
systems which contain unknown input signals. These unknown signals are modeled
as Gaussian processes (GP) with certain parametrized covariance structures. The
resulting latent force models (LFMs) can be seen as hybrid models that contain
a first-principles physical model part and a non-parametric GP model part. We
briefly review the statistical inference and learning methods for this kind of
models, introduce stochastic control methodology for the models, and provide
new theoretical observability and controllability results for them.
