On Some Tunable Multi-fidelity Bayesian Optimization Frameworks

• URL: http://arxiv.org/abs/2508.01013v1
• Date: Fri, 01 Aug 2025 18:26:39 GMT
• Title: On Some Tunable Multi-fidelity Bayesian Optimization Frameworks
• Authors: Arjun Manoj, Anastasia S. Georgiou, Dimitris G. Giovanis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis,
• Abstract summary: Multifidelity optimization employs surrogate models that integrate information from varying levels of fidelity.<n>We implement a proximity-based acquisition strategy that simplifies fidelity selection.<n>We also enable multi-fidelity Upper Confidence BoundUCB strategies by combining them with multi-fidelity GPs.
• Score: 0.35587965024910395
• License: http://creativecommons.org/licenses/by/4.0/
• 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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