• Computer Science > Machine Learning [Submitted on 4 Feb 2026] Title:Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization View PDF HTML (experimental)Abstract: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.
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- Summary
A new Bayesian optimization framework, POGPN‑JPSS, integrates process‑expert knowledge with structured probabilistic modeling to accelerate optimization of high‑dimensional, multi‑stage manufacturing systems. By representing the process as a Directed Acyclic Graph and extracting low‑dimensional latent features from high‑dimensional intermediate state‑space data, the method leverages intermediate observations that standard black‑box Bayesian optimization ignores. Applied to a simulated bioethanol production line, POGPN‑JPSS achieved the target performance twice as quickly and with higher reliability than leading alternatives, translating into significant time and resource savings. The study underscores the value of combining expert insight with advanced Gaussian‑process networks for rapid process maturation.
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