This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the optimization problem can be evaluated using either an accurate but time-consuming computer program or a fast lower-fidelity one. In this setting, the aim is to solve the optimization problem using as few calls to the high-fidelity program as possible. To this end, it is proposed to use Gaussian process models with trend functions built from the projection of low-fidelity solutions on a reduced-order basis synthesized from scarce high-fidelity snapshots. A study on the ability of such models to accurately represent the objective and the constraints and a comparison of two improvement-based infill strategies are performed on a representative benchmark test case.

中文翻译：

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使用非侵入式降阶基础的贝叶斯优化的约束多保真代理框架

本文解决了使用贝叶斯优化技术在多保真度（或可变复杂度）框架中的无导数约束优化问题。假定可以使用准确但耗时的计算机程序或快速低保真的计算机程序来评估优化问题中涉及的目标和约束。在这种情况下，目标是通过使用尽可能少的对高保真程序的调用来解决优化问题。为此，建议将高斯过程模型与趋势函数一起使用，这些趋势函数是根据稀疏的高保真快照合成的低阶解决方案的投影构建的低保真解决方案的投影而构建的。