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中,设计师的目标是在使用数字模拟器与优化方法环绕中找到对特定要求的最佳设计。在设计优化过程中,良好的设计优化过程是一个能够缩短从开始到设计的时间的过程。在这项工作中,我们采取一类设计问题,这种设计问题计算成本低,但具有高维设计空间。在这种情况下,传统的代用优化并不带来任何好处。在这项工作中,我们提议了一种替代ML模型,以替代设计过程,该设计过程将搜索问题作为一种反向问题形成,通过找到最佳设计或至少一个良好的优化初期种子设计来节省时间。通过使用这一经过培训的替代模型,我们可以用传统的优化方法获得两个世界的最佳结果。我们称之为“Surrrogate Apptimization(SAO)”-一种混合方法,将ML sudragate与传统的优化方法混合在一起。对螺旋桨设计问题进行的“Empicalalal”评估表明,在使用SAO的较少的评价中可以找到更高效的设计。</s>