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Operating large-scale scientific facilities often requires fast tuning androbust control in a high dimensional space. In this paper we introduce a newphysics-informed optimization algorithm based on Gaussian process regression.Our method takes advantage of the existing domain knowledge in the form ofrealizations of a physics model of the observed system. We have applied aphysics-informed Gaussian Process method experimentally at the SPEAR3 storagering to demonstrate online accelerator optimization. This method outperformsGaussian Process trained on data as well as the standard approach routinelyused for operation, in terms of convergence speed and optimal point. Theproposed method could be applicable to automatic tuning and control of othercomplex systems, without a prerequisite for any observed data.

Online tuning and light source control using a physics-informed Gaussian process Adi