  Bayesian optimization (BO) is one of the most effective methods for
closed-loop experimental design and black-box optimization. However, a key
limitation of BO is that it is an inherently sequential algorithm (one
experiment is proposed per round) and thus cannot directly exploit
high-throughput (parallel) experiments. Diverse modifications to the BO
framework have been proposed in the literature to enable exploitation of
parallel experiments but such approaches are limited in the degree of
parallelization that they can achieve and can lead to redundant experiments
(thus wasting resources and potentially compromising performance). In this
work, we present new parallel BO paradigms that exploit the structure of the
system to partition the design space. Specifically, we propose an approach that
partitions the design space by following the level sets of the performance
function and an approach that exploits partially-separable structures of the
performance function found. We conduct extensive numerical experiments using a
reactor case study to benchmark the effectiveness of these approaches against a
variety of state-of-the-art parallel algorithms reported in the literature. Our
computational results show that our approaches significantly reduce the
required search time and increase the probability of finding a global (rather
than local) solution.
