标题： 多目标气动外形设计优化的NASA通用研究模型 显示英文标题

标题： Multiobjective Aerodynamic Design Optimization of the NASA Common Research Model

摘要： 飞机气动设计优化必须考虑巡航段沿线变化的操作条件，而不是在固定操作条件下进行设计，以获得更现实的设计方案。 传统方法通过执行“多点”优化来解决这个问题，该方法假设在巡航段的一组子段上的目标的加权平均值。 我们认为，由于这种多点方法不可避免地受到权重规范的影响，它们可能导致次优设计。 相反，我们建议同时在多个子段上优化飞机设计——即通过多目标优化，从而得到一组帕累托最优解。 然而，现有的多目标优化工作存在以下问题：(i) 样本效率不足（即保持收敛时函数评估次数最少），(ii) 与输入维度和目标数量的可扩展性，以及(iii) 生成一批迭代以进行同步并行评估的能力。 为了克服这些限制，我们提出了一种新颖的多目标贝叶斯优化方法，与现有技术相比，该方法表现出改进的样本效率和准确性。 受汤普森采样启发，我们的方法利用高斯过程代理模型和贝叶斯决策理论，根据它们是帕累托最优的概率生成一系列迭代。 我们的方法名为批量帕累托最优汤普森采样（$q\texttt{POTS}$），在各种合成实验以及一个$24$维的双目标NASA通用研究模型气动设计优化中表现出优越的实验性能。 我们还提供了我们方法的开源软件。 显示英文摘要

摘要： Aircraft aerodynamic design optimization must account for the varying operating conditions along the cruise segment as opposed to designing at one fixed operating condition, to arrive at more realistic designs. Conventional approaches address this by performing a ``multi-point'' optimization that assumes a weighted average of the objectives at a set of sub-segments along the cruise segment. We argue that since such multi-point approaches are, inevitably, biased by the specification of the weights, they can lead to sub-optimal designs. Instead, we propose to optimize the aircraft design at multiple sub-segments simultaneously -- that is, via multiobjective optimization that leads to a set of Pareto optimal solutions. However, existing work in multiobjective optimization suffers from (i) lack of sample efficiency (that is, keeping the number of function evaluations to convergence minimal), (ii) scalability with input dimensions and number of objectives, and (iii) the ability to generate a batch of iterates for synchronous parallel evaluations. To overcome these limitations, we propose a novel multiobjective Bayesian optimization methodology that demonstrates improved sample efficiency and accuracy compared to the state of the art. Inspired by Thompson sampling, our approach leverages Gaussian process surrogates and Bayesian decision theory to generate a sequence of iterates according to the probability that they are Pareto optimal. Our approach, named batch Pareto optimal Thompson sampling ($q\texttt{POTS}$), demonstrates superior empirical performance on a variety of synthetic experiments as well as a $24$ dimensional two-objective aerodynamic design optimization of the NASA common research model. We also provide open-source software of our methodology.