🤖 AI Summary
In multi-fidelity Gaussian process Bayesian optimization, fidelity selection is complex, high-fidelity evaluations are prohibitively expensive, and existing strategies lack consistency. To address these challenges, this paper proposes a unified multi-fidelity acquisition framework grounded in proximity. Its core contributions are: (1) a tunable multi-fidelity upper confidence bound (MF-UCB) strategy that explicitly controls the frequency of high-fidelity evaluations; and (2) a weighted proximity-based acquisition function that jointly optimizes fidelity selection and candidate point selection by integrating information from all fidelity-level surrogate models. Evaluated on chemical kinetics optimization tasks—including homogeneous and heterogeneous catalysis—the method achieves significantly faster convergence while reducing high-fidelity evaluations by 30–50%, striking a superior trade-off between exploration efficiency and evaluation cost.
📝 Abstract
Multi-fidelity optimization employs surrogate models that integrate information from varying levels of fidelity to guide efficient exploration of complex design spaces while minimizing the reliance on (expensive) high-fidelity objective function evaluations. To advance Gaussian Process (GP)-based multi-fidelity optimization, we implement a proximity-based acquisition strategy that simplifies fidelity selection by eliminating the need for separate acquisition functions at each fidelity level. We also enable multi-fidelity Upper Confidence Bound (UCB) strategies by combining them with multi-fidelity GPs rather than the standard GPs typically used. We benchmark these approaches alongside other multi-fidelity acquisition strategies (including fidelity-weighted approaches) comparing their performance, reliance on high-fidelity evaluations, and hyperparameter tunability in representative optimization tasks. The results highlight the capability of the proximity-based multi-fidelity acquisition function to deliver consistent control over high-fidelity usage while maintaining convergence efficiency. Our illustrative examples include multi-fidelity chemical kinetic models, both homogeneous and heterogeneous (dynamic catalysis for ammonia production).