Structural optimization is part of the mechanical engineering field and, in most cases, tries to minimize the overall weight of a given design domain, subjected to functionality constraints given in terms of stresses of displacements. The most relevant techniques are topology and shape optimization. Topology optimization provides the optimal material distribution layout into a given, static, design domain. On the other hand, shape optimization provides the optimal combination of the parameters that define the required parametrization of the domain's boundary. Both techniques have strengths and weaknesses, thus a hybrid optimization approach that combines the former techniques will define a more general structural optimization framework that will take advantage of their synergistic combination. The difficulty arises when communicating both techniques for which, in this paper, we propose a machine learning-based methodology.

结构优化是机械工程领域的一部分，并且在大多数情况下，试图最小化给定设计域的总重量，受到位移应力方面的功能约束。最相关的技术是拓扑和形状优化。拓扑优化为给定的静态设计域提供了最佳的材料分布布局。另一方面，形状优化提供了定义域边界所需参数化的参数的最佳组合。两种技术都有优点和缺点，因此结合前一种技术的混合优化方法将定义一个更通用的结构优化框架，该框架将利用它们的协同组合。