Quality diversity algorithms can be used to efficiently create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations. Even with the assistance of surrogate models, quality diversity needs 100s or even 1000s of evaluations, which can make it use infeasible. In this study we try to tackle this problem by using a pre-optimization strategy on a lower-dimensional optimization problem and then map the solutions to a higher-dimensional case. For a use case to design buildings that minimize wind nuisance, we show that we can predict flow features around 3D buildings from 2D flow features around building footprints. For a diverse set of building designs, by sampling the space of 2D footprints with a quality diversity algorithm, a predictive model can be trained that is more accurate than when trained on a set of footprints that were selected with a space-filling algorithm like the Sobol sequence. Simulating only 16 buildings in 3D, a set of 1024 building designs with low predicted wind nuisance is created. We show that we can produce better machine learning models by producing training data with quality diversity instead of using common sampling techniques. The method can bootstrap generative design in a computationally expensive 3D domain and allow engineers to sweep the design space, understanding wind nuisance in early design phases.

翻译：质量多样性算法可高效生成多样化解决方案，以辅助工程师建立直觉。但对于代价极高的问题，质量多样性算法效率低下——需数十万次评估。即便借助代理模型，该方法仍需数百甚至数千次评估，导致其应用不可行。本研究通过采用低维优化问题的预优化策略，将解映射至高维案例以解决此问题。针对旨在最小化风扰的建筑设计案例，我们证明：基于建筑足迹的二维流场特征可预测三维建筑周围流场特征。通过质量多样性算法采样二维足迹空间训练预测模型，其精度优于采用Sobol序列等空间填充算法选取足迹数据训练的模型。仅模拟16座三维建筑，即可生成含1024种低风扰预测值的建筑设计方案。研究表明，采用质量多样性算法生成训练数据，比常规采样技术更有利于构建高性能机器学习模型。该方法可引导计算成本高昂的三维领域生成式设计，使工程师在设计早期阶段遍历设计空间，理解风扰特征。