  Physical simulation-based optimization is a common task in science and
engineering. Many such simulations produce image- or tensor-based outputs where
the desired objective is a function of those outputs, and optimization is
performed over a high-dimensional parameter space. We develop a Bayesian
optimization method leveraging tensor-based Gaussian process surrogates and
trust region Bayesian optimization to effectively model the image outputs and
to efficiently optimize these types of simulations, including a radio-frequency
tower configuration problem and an optical design problem.
