New RS-MFBO framework optimizes industrial process simulations

Researchers have developed a new framework called Reduced-Space Multi-Fidelity Bayesian Optimization (RS-MFBO) to tackle the computational challenges of optimizing industrial process simulations. This method integrates Global Sensitivity Analysis for dimensionality reduction with a Gaussian process that accounts for correlations between low-cost and high-fidelity evaluations. An adaptive acquisition strategy manages sample allocation across different fidelity levels, and the framework has been successfully tested on bioprocess and fuel synthesis simulators, demonstrating significant reductions in high-fidelity evaluations while maintaining competitive optimization performance. AI

IMPACT This framework offers a scalable, simulator-agnostic approach for cost-constrained black-box optimization in industrial processes.

RANK_REASON The cluster contains a research paper detailing a new optimization framework. [lever_c_demoted from research: ic=1 ai=1.0]

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• Aspen HYSYS
• Gaussian process
• Niki Triantafyllou
• RS-MFBO
• SuperPro Designer

• paper
• infra

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