A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications
A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications
复制标题
用于工程设计应用的监督机器学习分类技术的比较评估
DOI：
10.1115/1.4044524
复制
发表时间：
2019
影响因子：
3.3
通讯作者：
Seepersad, Carolyn Conner
中科院分区：
文献类型：
--
作者：
Sharpe, Conner;Wiest, Tyler;Wang, Pingfeng;Seepersad, Carolyn Conner
监督机器学习技术已被证明是工程设计探索和优化应用的有效工具，其中它们对于映射设计空间的有希望或可行区域特别有用。设计空间映射可用于通知早期阶段的设计探索，提供可靠性评估，并有助于在需要协作设计团队的多目标或多级问题的收敛。然而，映射的准确性可以基于问题因素而变化，例如设计变量的数量、离散变量的存在、底层响应函数的多模态以及可用的训练数据的量。此外，还有几种有用的机器学习算法，每种算法都有自己的一组算法超参数，这些参数会显著影响准确性和计算费用。这项工作阐明了使用机器学习的工程设计探索和优化问题，通过调查流行的分类算法的性能在各种示例工程优化问题。结果被合成为一组观察结果，为工程师提供将这些技术应用于未来问题的直觉，以及基于问题类型的建议，以帮助工程师选择和利用算法。
Supervised machine learning techniques have proven to be effective tools for engineering design exploration and optimization applications, in which they are especially useful for mapping promising or feasible regions of the design space. The design space mappings can be used to inform early-stage design exploration, provide reliability assessments, and aid convergence in multiobjective or multilevel problems that require collaborative design teams. However, the accuracy of the mappings can vary based on problem factors such as the number of design variables, presence of discrete variables, multimodality of the underlying response function, and amount of training data available. Additionally, there are several useful machine learning algorithms available, and each has its own set of algorithmic hyperparameters that significantly affect accuracy and computational expense. This work elucidates the use of machine learning for engineering design exploration and optimization problems by investigating the performance of popular classification algorithms on a variety of example engineering optimization problems. The results are synthesized into a set of observations to provide engineers with intuition for applying these techniques to their own problems in the future, as well as recommendations based on problem type to aid engineers in algorithm selection and utilization.
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影响因子：
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影响因子：
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