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.

翻译：贝叶斯优化（Bayesian optimization, BO）是一种用于优化含噪声、评估代价高昂的黑箱函数的强大技术，在科学、工程、经济学、制造业等领域具有广泛的实际应用。本文综述了面向下一代过程系统设计时，BO方法的最新发展、挑战与机遇。在描述多个激励性应用案例后，我们讨论了如何开发先进BO方法以更高效地解决这些应用中的重要问题。最后，本文总结了在提升概率模型质量、选择下一个采样点的内部优化程序、以及利用问题结构提升采样效率等方面的挑战与机遇。