Bayesian optimization (BO) is a powerful technology for optimizing noisy
expensive-to-evaluate black-box functions, with a broad range of real-world
applications in science, engineering, economics, manufacturing, and beyond. In
this paper, we provide an overview of recent developments, challenges, and
opportunities in BO for design of next-generation process systems. After
describing several motivating applications, we discuss how advanced BO methods
have been developed to more efficiently tackle important problems in these
applications. We conclude the paper with a summary of challenges and
opportunities related to improving the quality of the probabilistic model, the
choice of internal optimization procedure used to select the next sample point,
and the exploitation of problem structure to improve sample efficiency.

贝叶斯优化是一种强大的技术，可用于优化噪声大、昂贵难评估的黑箱函数，在科学、工程、经济、制造等领域具有广泛的实际应用。本文概述了贝叶斯优化在下一代过程系统设计中的最新进展、挑战和机遇，并介绍了如何利用高级贝叶斯优化方法更有效地解决这些应用中的重要问题。最后，我们总结了提高概率模型质量、选择下一个样本点的内部优化过程以及利用问题结构提高样本效率方面的挑战和机会。