In computer-aided engineering design, the goal of a designer is to find an optimal design on a given requirement using the numerical simulator in loop with an optimization method. In this design optimization process, a good design optimization process is one that can reduce the time from inception to design. In this work, we take a class of design problem, that is computationally cheap to evaluate but has high dimensional design space. In such cases, traditional surrogate-based optimization does not offer any benefits. In this work, we propose an alternative way to use ML model to surrogate the design process that formulates the search problem as an inverse problem and can save time by finding the optimal design or at least a good initial seed design for optimization. By using this trained surrogate model with the traditional optimization method, we can get the best of both worlds. We call this as Surrogate Assisted Optimization (SAO)- a hybrid approach by mixing ML surrogate with the traditional optimization method. Empirical evaluations of propeller design problems show that a better efficient design can be found in fewer evaluations using SAO.

翻译：在计算机辅助工程设计领域，设计人员的目标是在给定需求下，通过数值模拟器与优化方法的循环迭代，找到最优设计。在此设计优化过程中，一个优秀的设计优化流程应能缩短从概念构思到最终设计的时间。本研究针对一类设计问题展开：其计算评估成本较低，但设计空间维度较高。在此类情况下，传统基于代理模型的优化方法并无明显优势。本文提出一种替代方案——利用机器学习模型替代设计过程，将搜索问题构建为逆问题，通过寻找最优设计或至少为优化提供良好的初始种子设计来节省时间。将经过训练的代理模型与传统优化方法相结合，可兼得两者之长。我们称其为代理辅助优化（SAO）——一种融合机器学习代理与传统优化方法的混合策略。螺旋桨设计问题的实证评估表明，采用SAO方法能以更少的评估次数找到更高效的设计。