  Operating large-scale scientific facilities often requires fast tuning and
robust control in a high dimensional space. In this paper we introduce a new
physics-informed optimization algorithm based on Gaussian process regression.
Our method takes advantage of the existing domain knowledge in the form of
realizations of a physics model of the observed system. We have applied a
physics-informed Gaussian Process method experimentally at the SPEAR3 storage
ring to demonstrate online accelerator optimization. This method outperforms
Gaussian Process trained on data as well as the standard approach routinely
used for operation, in terms of convergence speed and optimal point. The
proposed method could be applicable to automatic tuning and control of other
complex systems, without a prerequisite for any observed data.
