论文标题
了解高参数优化对机器学习模型的结构设计问题的影响
Understanding the effect of hyperparameter optimization on machine learning models for structure design problems
论文作者
论文摘要
为了使用昂贵的有限元模拟来减轻设计评估的计算成本，替代模型已被广泛应用于计算机辅助工程设计。机器学习算法（MLA）已作为替代模型实现，因为它们能够学习设计变量与大数据集的响应之间的复杂相互关系。通常，MLA回归模型包含模型参数和超参数。通过拟合训练数据获得模型参数。管理模型结构和培训过程的超参数是在培训之前由用户分配的。缺乏关于超参数对代理模型准确性和鲁棒性的影响的系统研究。在这项工作中，我们建议建立超参数优化（HOPT）框架，以加深我们对效果的理解。在四个基准示例上测试了四个经常使用的MLA，即高斯过程回归（GPR），支持向量机（SVM），随机森林回归（RFR）和人工神经网络（ANN）。对于每个MLA模型，比较HOPT之前和之后的模型准确性和鲁棒性。结果表明，Hopt通常可以提高MLA模型的性能。霍普（Hopt）对复杂问题的MLAS准确性和鲁棒性几乎没有提高，而高维混合变异的设计空间则具有。建议将HOPT用于中间复杂性的设计问题。我们还研究了Hopt产生的额外计算成本。培训成本与MLA体系结构密切相关。 Hopt之后，ANN和RFR的培训成本比GPR和SVM的培训成本高。综上所述，这项研究使Hopt方法的选择受益于根据其复杂性的不同类型的设计问题。
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.
