On Some Tunable Multi-fidelity Bayesian Optimization Frameworks
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| author | Manoj, Arjun Georgiou, Anastasia S. Giovanis, Dimitris G. Sapsis, Themistoklis P. Kevrekidis, Ioannis G. |
| author_facet | Manoj, Arjun Georgiou, Anastasia S. Giovanis, Dimitris G. Sapsis, Themistoklis P. Kevrekidis, Ioannis G. |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01013 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On Some Tunable Multi-fidelity Bayesian Optimization Frameworks Manoj, Arjun Georgiou, Anastasia S. Giovanis, Dimitris G. Sapsis, Themistoklis P. Kevrekidis, Ioannis G. Machine Learning Artificial Intelligence Optimization and Control 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). |
| title | On Some Tunable Multi-fidelity Bayesian Optimization Frameworks |
| topic | Machine Learning Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2508.01013 |