The paper introduces BO-pro-c, a Bayesian optimisation algorithm that employs a product-of-experts Gaussian process (GP-pro-c) as its surrogate model. GP-pro-c combines multiple local GP experts to improve uncertainty quantification, reduce computational cost, and preserve global correlations, addressing the cubic complexity of single global GP models. Experiments show that BO-pro-c achieves competitive optimisation performance with a 0.9% lower simple regret and a 39.4% reduction in computational overhead compared to a single‑global‑GP baseline.
By Yean Hoon Ong
Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensionality inherent in complex design spaces. To address...
arXiv:2609.17440v1 Announce Type: new
Abstract: Optimizing industrial process flowsheets is often computationally prohibitive due to the high cost of rigorous simulations and the curse of dimensional...
By Niki Triantafyllou, Andrea Bernardi, Maria M. Papathanasiou
arXiv:2508.10970v2 Announce Type: replace-cross
Abstract: Bioprocesses are central to modern biotechnology, enabling sustainable production of pharmaceuticals, specialty chemicals, cosmetics, and foo...
By Adrian Martens, Mathias Neufang, Alessandro Butt\'e, Moritz von Stosch, Antonio del Rio Chanona, Laura Marie Helleckes
arXiv:2502. 18966v2 Announce Type: replace Abstract: General chemical reaction conditions that achieve consistently high performance across multiple substrates are important for practical applications such as library synthesis and high-throughput experimentation.
By Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Al\'an Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff
arXiv:2607. 23404v1 Announce Type: new Abstract: Self-driving laboratories increasingly rely on multi-fidelity Bayesian optimization (MFBO) to balance cheap, approximate evaluations against scarce, expensive ones, with a predictive surrogate at its core.
By Jaewook Lee, Ethan Errington, Christian D. Lorenz, Miao Guo