Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization

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 that image with respect to a high-dimensional parameter space. %some parameters. 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 an optical design problem and a radio-frequency tower configuration problem.