Title: Constrained Bayesian Optimization under Bivariate Gaussian Process with Application to Cure Process Optimization Show Chinese title

Title: 约束贝叶斯优化在双变量高斯过程中的应用——固化工艺优化

Abstract: Bayesian Optimization, leveraging Gaussian process models, has proven to be a powerful tool for minimizing expensive-to-evaluate objective functions by efficiently exploring the search space. Extensions such as constrained Bayesian Optimization have further enhanced Bayesian Optimization's utility in practical scenarios by focusing the search within feasible regions defined by a black-box constraint function. However, constrained Bayesian Optimization in is developed based on the independence Gaussian processes assumption between objective and constraint functions, which may not hold in real-world applications. To address this issue, we use the bivariate Gaussian process model to characterize the dependence between the objective and constraint functions and developed the constrained expected improvement acquisition function under this model assumption. We show case the performance of the proposed approach with an application to cure process optimization in Manufacturing. Show Chinese abstract

Abstract: 基于高斯过程模型的贝叶斯优化已被证明是一种强大的工具，能够在高效探索搜索空间的同时最小化昂贵的目标函数评估。扩展形式如受限贝叶斯优化通过聚焦于由黑盒约束函数定义的可行区域内的搜索，进一步增强了贝叶斯优化在实际场景中的实用性。然而，现有的受限贝叶斯优化建立在目标函数和约束函数之间独立的高斯过程假设之上，在现实世界的应用中这一假设可能不成立。为了解决这个问题，我们使用双变量高斯过程模型来刻画目标函数与约束函数之间的依赖关系，并在此模型假设下开发了受限期望提升获取函数。我们通过一个在制造业固化工艺优化中的应用案例展示了所提出方法的性能。