A Constrained Multi-Fidelity Bayesian Optimization Method

• URL: http://arxiv.org/abs/2510.10984v1
• Date: Mon, 13 Oct 2025 03:45:54 GMT
• Title: A Constrained Multi-Fidelity Bayesian Optimization Method
• Authors: Jingyi Wang, Nai-Yuan Chiang, Tucker Hartland, J. Luc Peterson, Jerome Solberg, Cosmin G. Petra,
• Abstract summary: We propose a constrained multi-fidelity Bayesian optimization (CMFBO) method with novel acquisition functions.<n>Specifically, we design efficient acquisition functions that 1) have analytically closed-form expressions; 2) are straightforward to implement; and 3) do not require feasible initial samples.<n>We demonstrate the effectiveness of our algorithms on synthetic test problems using different combinations of acquisition functions.
• Score: 2.038050035201515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, multi-fidelity Bayesian optimization (MFBO) has been successfully applied to many engineering design optimization problems, where the cost of high-fidelity simulations and experiments can be prohibitive. However, challenges remain for constrained optimization problems using the MFBO framework, particularly in efficiently identifying the feasible region defined by the constraints. In this paper, we propose a constrained multi-fidelity Bayesian optimization (CMFBO) method with novel acquisition functions. Specifically, we design efficient acquisition functions that 1) have analytically closed-form expressions; 2) are straightforward to implement; and 3) do not require feasible initial samples, an important feature often missing in commonly used acquisition functions such as expected constrained improvement (ECI). We demonstrate the effectiveness of our algorithms on synthetic test problems using different combinations of acquisition functions. Then, we apply the proposed method to a data-driven inertial confinement fusion (ICF) design problem, and a high-current joint design problem using finite element simulations with computational contact mechanics.

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