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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.
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This content will become publicly available on July 1, 2026
Nonadaptive Stochastic Score Classification and Explainable Half-Space Evaluation
Nonadaptive Stochastic Score Classification Sequential testing problems involve a system with several components, each of which is working with some independent probability. The working/failed status of each component can be determined by performing a test, which is usually expensive. So, the goal is to perform tests in a carefully chosen sequence until the overall system status can be evaluated. These problems arise in a variety of applications, such as healthcare, manufacturing, and telecommunication. A common task in these applications is to categorize the system into one of several classes that correspond to the system status being poor, fair, good, excellent, etc. In “Nonadaptive Stochastic Score Classification and Explainable Half-Space Evaluation,” Ghuge, Gupta, and Nagarajan provide the first constant-factor approximation algorithm for this problem. Moreover, the resulting policy is nonadaptive, which results in significant savings in computational time. The authors also validate their theoretical results via computational experiments, where they observe that their algorithm’s cost is on average at most 50% more than an information-theoretic lower bound.
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- PAR ID:
- 10631736
- Publisher / Repository:
- INFORMS
- Date Published:
- Journal Name:
- Operations Research
- Volume:
- 73
- Issue:
- 4
- ISSN:
- 0030-364X
- Page Range / eLocation ID:
- 2204 to 2222
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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