Gaussian Process Latent Force Models for Learning and Stochastic Control of Physical Systems
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This paper is concerned with estimation and stochastic control in physical systems which contain unknown input signals or forces. These unknown signals are modeled as Gaussian processes (GP) in the sense that GP models are used in machine learning. 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. The aim of this paper is to collect and extend the statistical inference and learning methods for this kind of models, provide new theoretical results for the models, and to extend the methodology and theory to stochastic control of LFMs.
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