To relieve the computational cost of design evaluations using expensive finite element simulations, surrogate models have been widely applied in computer-aided engineering design. Machine learning algorithms (MLAs) have been implemented as surrogate models due to their capability of learning the complex interrelations between the design variables and the response from big datasets. Typically, an MLA regression model contains model parameters and hyperparameters. The model parameters are obtained by fitting the training data. Hyperparameters, which govern the model structures and the training processes, are assigned by users before training. There is a lack of systematic studies on the effect of hyperparameters on the accuracy and robustness of the surrogate model. In this work, we proposed to establish a hyperparameter optimization (HOpt) framework to deepen our understanding of the effect. Four frequently used MLAs, namely Gaussian Process Regression (GPR), Support Vector Machine (SVM), Random Forest Regression (RFR), and Artificial Neural Network (ANN), are tested on four benchmark examples. For each MLA model, the model accuracy and robustness before and after the HOpt are compared. The results show that HOpt can generally improve the performance of the MLA models in general. HOpt leads to few improvements in the MLAs accuracy and robustness for complex problems, which are featured by high-dimensional mixed-variable design space. The HOpt is recommended for the design problems with intermediate complexity. We also investigated the additional computational costs incurred by HOpt. The training cost is closely related to the MLA architecture. After HOpt, the training cost of ANN and RFR is increased more than that of the GPR and SVM. To sum up, this study benefits the selection of HOpt method for the different types of design problems based on their complexity.

翻译：为了减轻使用昂贵的有限要素模拟进行设计评价的计算成本,在计算机辅助工程设计中广泛应用了替代模型。机械学习算法(MLAs)作为替代模型,因为它们有能力学习设计变量与大数据集响应之间的复杂相互关系。典型的情况是,司法协助回归模型包含模型参数和超参数。模型参数是通过匹配培训数据获得的。管理模型结构和培训过程的超参数由用户在培训前指定。缺乏关于超参数对代孕模型的准确性和稳健性的影响的系统研究。在这项工作中,我们提议建立一个超参数优化(Hopt)框架,以加深我们对影响的理解。四种常用的司法协助模型,即Gaussian进程回归(GPR),支持Vectoral Forest Regilation(RFRR),以及人工神经网络(ANNU),在四个基准示例上进行测试。对于每部的模型,前一个模型的准确性和稳健的精确度,在GROML设计之后,其总体设计方法的精确性能显示其成本的精确性。在GRML的精确性设计中,其总体设计方法的精确性成本中可以用来比较。在GILIL的精确性模型中,其总的精确性模型中可以显示其结果的精确度的精确度上,可以比较。