{"ID":2888099,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.01013","arxiv_id":"2508.01013","title":"On Some Tunable Multi-fidelity Bayesian Optimization Frameworks","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).","short_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 optimiz...","url_abs":"https://arxiv.org/abs/2508.01013","url_pdf":"https://arxiv.org/pdf/2508.01013v1","authors":"[\"Arjun Manoj\",\"Anastasia S. Georgiou\",\"Dimitris G. Giovanis\",\"Themistoklis P. Sapsis\",\"Ioannis G. Kevrekidis\"]","published":"2025-08-01T18:26:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"math.OC\"]","methods":"[\"LoRA\"]","has_code":false}