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arXiv · 2508.01013

On Some Tunable Multi-fidelity Bayesian Optimization Frameworks

Arjun Manoj·Anastasia S. Georgiou·Dimitris G. Giovanis·Themistoklis P. Sapsis·Ioannis G. Kevrekidis
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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).

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Arjun Manoj, Anastasia S. Georgiou, Dimitris G. Giovanis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis. 2025-08-01. On Some Tunable Multi-fidelity Bayesian Optimization Frameworks. https://arxiv.org/abs/2508.01013

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