AI 中文总结
提出含高斯过程代理、贝叶斯优化与局部模型的双层方法GLORO，在RAE2822翼型优化中实现鲁棒升阻比，解决含昂贵评估的鲁棒优化问题。
AI 中文摘要
我们提出GLORO，一种适用于含昂贵函数评估的鲁棒优化的双层求解方法。该方法经精心设计，可在计算效率下获得满意的优化结果，其基于参数空间中的高斯过程代理模型，通过贝叶斯优化（下层）近似计算鲁棒优化问题的目标/约束，再利用这些近似评估在变量空间构建局部模型（上层）。使用参数空间的贝叶斯优化和变量空间的局部模型，均是为了引导昂贵函数评估至优化过程的关注区域。该方法以鲁棒翼型设计优化问题为动机并在该问题上测试，应用聚焦于RAE2822翼型，优化其外形以确保在马赫数和攻角的运行不确定性下具有鲁棒的升阻比。
英文摘要
We propose GLORO, a bilevel solution methodology for robust optimization involving expensive function evaluations. The methodology is carefully designed to achieve satisfactory optimization results in a computationally efficient manner. It is based on Gaussian Process surrogates in the parameter space for approximate computations of the objective / constraints of the robust optimization problem via Bayesian optimization (lower level), and local models constructed in the variable space using these approximate evaluations (upper level). Both the use of Bayesian optimization (in parameter space) and the use of local models (in variable space) are motivated by the idea of guiding the expensive function evaluations to the regions of interest for the optimization process. The methodological work is motivated by and tested on a robust airfoil design optimization problem. This application focuses on the RAE2822 airfoil, optimizing its shape to ensure a robust lift-to-drag ratio under operational uncertainties in Mach number and angle of attack.
