  High-dimensional optimization is a critical challenge for operating
large-scale scientific facilities. We apply a physics-informed Gaussian process
(GP) optimizer to tune a complex system by conducting efficient global search.
Typical GP models learn from past observations to make predictions, but this
reduces their applicability to new systems where archive data is not available.
Instead, here we use a fast approximate model from physics simulations to
design the GP model. The GP is then employed to make inferences from sequential
online observations in order to optimize the system. Simulation and
experimental studies were carried out to demonstrate the method for online
control of a storage ring. We show that the physics-informed GP outperforms
current routinely used online optimizers in terms of convergence speed, and
robustness on this task. The ability to inform the machine-learning model with
physics may have wide applications in science.
