联合参数与状态空间贝叶斯优化：利用过程专业知识加速制造优化
Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
AI总结：
本文提出POGPN-JPSS框架，结合POGPN与联合参数状态空间建模，利用专家知识提取低维特征，提升多阶段生物乙醇生产过程优化效率。
摘要：
贝叶斯优化（BO）是一种强大的方法，用于优化黑盒制造过程，但其性能在处理高维多阶段系统时受限，特别是当可以观察到中间输出时。标准BO将过程视为黑盒并忽略中间观察和底层过程结构。部分可观测高斯过程网络（POGPN）将过程建模为有向无环图（DAG）。然而，当观测是高维状态空间时间序列时，使用中间观测具有挑战性。过程专家知识可用于从高维状态空间数据中提取低维潜在特征。我们提出了POGPN-JPSS框架，结合POGPN与联合参数和状态空间（JPSS）建模，以利用提取的中间信息。我们在具有挑战性的高维多阶段生物乙醇生产过程模拟中展示了POGPN-JPSS的有效性。我们的结果表明，POGPN-JPSS显著优于现有方法，通过在两倍时间内达到所需性能阈值并更具可靠性。快速优化直接转化为时间和资源的显著节省。这突显了将专家知识与结构化概率模型结合以实现快速过程成熟的重要性。
英文摘要：
Bayesian optimization (BO) is a powerful method for optimizing black-box manufacturing processes, but its performance is often limited when dealing with high-dimensional multi-stage systems, where we can observe intermediate outputs. Standard BO models the process as a black box and ignores the intermediate observations and the underlying process structure. Partially Observable Gaussian Process Networks (POGPN) model the process as a Directed Acyclic Graph (DAG). However, using intermediate observations is challenging when the observations are high-dimensional state-space time series. Process-expert knowledge can be used to extract low-dimensional latent features from the high-dimensional state-space data. We propose POGPN-JPSS, a framework that combines POGPN with Joint Parameter and State-Space (JPSS) modeling to use intermediate extracted information. We demonstrate the effectiveness of POGPN-JPSS on a challenging, high-dimensional simulation of a multi-stage bioethanol production process. Our results show that POGPN-JPSS significantly outperforms state-of-the-art methods by achieving the desired performance threshold twice as fast and with greater reliability. The fast optimization directly translates to substantial savings in time and resources. This highlights the importance of combining expert knowledge with structured probabilistic models for rapid process maturation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
