Bayesian optimization (BO) is a popular method for solving optimization problems involving expensive objective functions. Although BO has been applied across various fields, its use in structural optimization area is in its early stages. Origami folding structures provide a complex design space where the use of an efficient optimizer is critical. In this work for the first time we demonstrate the ability of BO to solve origami-inspired design problems. We use a Gaussian process (GP) as the surrogate model that is trained to mimic the response of the expensive finite element (FE) objective function. The ability of this BO-FE framework to find optimal designs is verified by applying it to well-known origami design problems. We compare the performance of the proposed approach to traditional gradient-based optimization techniques and genetic algorithm methods in terms of ability to discover designs and computational efficiency. BO has many user-defined components/parameters and intuitions for these for structural optimization are currently limited. In this work, we study the role of hyperparameter tuning and the sensitivity of Bayesian optimization to the quality and size of the initial training set. Taking a holistic view of the computational expense, we propose various heuristic approaches to reduce the overall cost of optimization. Our results show that Bayesian optimization is an efficient alternative to traditional methods. It allows for the discovery of optimal designs using fewer finite element solutions, which makes it an attractive choice for the non-convex design space of origami fold mechanics.

贝叶斯优化（BO）是解决包含昂贵目标函数的优化问题的一种流行方法。尽管BO已应用于各个领域，但其在结构优化领域的使用仍处于早期阶段。折纸折叠结构提供了一个复杂的设计空间，而使用高效的优化器至关重要。在这项工作中，我们首次展示了BO解决折纸启发的设计问题的能力。我们使用高斯过程（GP）作为替代模型，该模型经过训练以模仿昂贵的有限元（FE）目标函数的响应。通过将BO-FE框架应用于著名的折纸设计问题，可以验证其发现最佳设计的能力。我们在发现设计能力和计算效率方面，比较了所提出的方法与传统的基于梯度的优化技术和遗传算法方法的性能。BO具有许多用户定义的组件/参数，而用于结构优化的直觉目前受到限制。在这项工作中，我们研究了超参数调整的作用以及贝叶斯优化对初始训练集的质量和大小的敏感性。从计算费用的整体角度来看，我们提出了各种启发式方法来减少优化的总成本。我们的结果表明，贝叶斯优化是传统方法的有效替代方案。它允许使用更少的有限元解决方案来发现最佳设计，