A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 03, 2019
- Source ID
- 10.1115/1.4044524
Entities
People
- Carolyn Conner Seepersad
- Conner Sharpe
- Pingfeng Wang
- Tyler Wiest
Organizations
- Defense Advanced Research Projects Agency
- Lawrence Livermore National Laboratory
- National Science Foundation
- University of Illinois Urbana–Champaign
- University of Texas at Austin