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[2602.17679] Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization
Summary
This article presents a novel Bayesian optimization framework, POGPN-JPSS, that integrates process expertise to enhance the efficiency of optimizing high-dimensional manufacturing processes, demonstrating significant performance improvements in bioethanol production.
Why It Matters
The research addresses a critical challenge in manufacturing optimization by leveraging expert knowledge and advanced modeling techniques. This approach not only accelerates optimization processes but also leads to substantial resource savings, making it highly relevant for industries seeking efficiency in production.
Key Takeaways
- POGPN-JPSS combines expert knowledge with Bayesian optimization for better results.
- The framework significantly outperforms traditional methods in multi-stage systems.
- Utilizing intermediate observations enhances the optimization process.
- Faster optimization translates to reduced time and resource expenditure.
- The study emphasizes the importance of integrating structured models with process expertise.
Computer Science > Machine Learning arXiv:2602.17679 (cs) [Submitted on 4 Feb 2026] Title:Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization Authors:Saksham Kiroriwal, Julius Pfrommer, Jürgen Beyerer View a PDF of the paper titled Joint Parameter and State-Space Bayesian Optimization: Using Process Expertise to Accelerate Manufacturing Optimization, by Saksham Kiroriwal and 2 other authors 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. We demonstrate the effectiveness of POGPN-JPSS on a challenging, high-dimensional simulation of a multi-stage bioethanol productio...
