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This article is concerned with learning and stochastic control in physicalsystems which contain unknown input signals. These unknown signals are modeledas Gaussian processes (GP) with certain parametrized covariance structures. Theresulting latent force models (LFMs) can be seen as hybrid models that containa first-principles physical model part and a non-parametric GP model part. Webriefly review the statistical inference and learning methods for this kind ofmodels, introduce stochastic control methodology for the models, and providenew theoretical observability and controllability results for them.

Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems