Reduced-Space Multi-Fidelity Bayesian Optimization of Process Simulation Models
RS-MFBO couples global sensitivity analysis with fidelity-augmented Gaussian processes to slash costly high-fidelity simulation runs in industrial flowsheet optimization.
The paper presents RS-MFBO, a reduced-space multi-fidelity Bayesian optimization framework for high-dimensional, expensive black-box functions. It integrates Global Sensitivity Analysis for dimensionality reduction with a fidelity-augmented Gaussian process and a cost-aware acquisition strategy featuring cooldown and promotion mechanisms. Validation on a plasmid DNA bioprocess (SuperPro Designer) and a green fuel synthesis plant (Aspen HYSYS) shows substantial reductions in high-fidelity evaluations while remaining competitive with single-fidelity baselines.
- Global Sensitivity Analysis reduces dimensionality of high-dimensional black-box design spaces.
- Fidelity-augmented Gaussian process captures correlations between cheap and expensive evaluations.
- Cost-aware acquisition with cooldown and promotion allocates samples across fidelities.
- Validated on SuperPro Designer bioprocess and Aspen HYSYS fuel plant.
From the original article
1 min read at arxiv.orgOptimizing 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 these challenges, we present a reduced-space multi-fidelity Bayesian optimization (RS-MFBO) framework designed for high-dimensional, expensive black-box functions.
By Niki Triantafyllou · arxiv.org
A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.