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Adaptive sampling with automatic stopping for feasible region identification in engineering design

Jixiang Qing (UGent) , Nicolas Knudde (UGent) , Federico Garbuglia (UGent) , Domenico Spina (UGent) , Ivo Couckuyt (UGent) and Tom Dhaene (UGent)
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Abstract
Engineering design is a complex process to find a suitable trade-off among different, and sometimes conflicting, design specifications. In reality, these requirements can be often considered as constraints of the design problem, that can be defined in terms of performance measures or geometrical characteristics of the device under study. In this paper, a new design space exploration methodology is presented for discovering feasible regions in the design space, where the term feasible region indicates the set of all design configurations satisfying all constraints of the design problem. The proposed method is based on Gaussian process metamodels to estimate the feasible region and leverages a information-based adaptive sampling technique to sequentially refine the prediction accuracy, which is applicable for multiple constraints problems. To efficiently stop the adaptive sampling process, a novel framework to estimate the metamodel's prediction accuracy is proposed. The efficiency, accuracy and robustness of the proposed approach are compared with state-of-art techniques on suitable benchmark problems and practical engineering examples.
Keywords
OPTIMIZATION, ALGORITHM, Feasible region, Gaussian process, Adaptive sampling, Stopping criterion

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Citation

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MLA
Qing, Jixiang, et al. “Adaptive Sampling with Automatic Stopping for Feasible Region Identification in Engineering Design.” ENGINEERING WITH COMPUTERS, 2021, doi:10.1007/s00366-021-01341-7.
APA
Qing, J., Knudde, N., Garbuglia, F., Spina, D., Couckuyt, I., & Dhaene, T. (2021). Adaptive sampling with automatic stopping for feasible region identification in engineering design. ENGINEERING WITH COMPUTERS. https://doi.org/10.1007/s00366-021-01341-7
Chicago author-date
Qing, Jixiang, Nicolas Knudde, Federico Garbuglia, Domenico Spina, Ivo Couckuyt, and Tom Dhaene. 2021. “Adaptive Sampling with Automatic Stopping for Feasible Region Identification in Engineering Design.” ENGINEERING WITH COMPUTERS. https://doi.org/10.1007/s00366-021-01341-7.
Chicago author-date (all authors)
Qing, Jixiang, Nicolas Knudde, Federico Garbuglia, Domenico Spina, Ivo Couckuyt, and Tom Dhaene. 2021. “Adaptive Sampling with Automatic Stopping for Feasible Region Identification in Engineering Design.” ENGINEERING WITH COMPUTERS. doi:10.1007/s00366-021-01341-7.
Vancouver
1.
Qing J, Knudde N, Garbuglia F, Spina D, Couckuyt I, Dhaene T. Adaptive sampling with automatic stopping for feasible region identification in engineering design. ENGINEERING WITH COMPUTERS. 2021;
IEEE
[1]
J. Qing, N. Knudde, F. Garbuglia, D. Spina, I. Couckuyt, and T. Dhaene, “Adaptive sampling with automatic stopping for feasible region identification in engineering design,” ENGINEERING WITH COMPUTERS, 2021.
@article{8704547,
  abstract     = {{Engineering design is a complex process to find a suitable trade-off among different, and sometimes conflicting, design specifications. In reality, these requirements can be often considered as constraints of the design problem, that can be defined in terms of performance measures or geometrical characteristics of the device under study. In this paper, a new design space exploration methodology is presented for discovering feasible regions in the design space, where the term feasible region indicates the set of all design configurations satisfying all constraints of the design problem. The proposed method is based on Gaussian process metamodels to estimate the feasible region and leverages a information-based adaptive sampling technique to sequentially refine the prediction accuracy, which is applicable for multiple constraints problems. To efficiently stop the adaptive sampling process, a novel framework to estimate the metamodel's prediction accuracy is proposed. The efficiency, accuracy and robustness of the proposed approach are compared with state-of-art techniques on suitable benchmark problems and practical engineering examples.}},
  author       = {{Qing, Jixiang and Knudde, Nicolas and Garbuglia, Federico and Spina, Domenico and Couckuyt, Ivo and Dhaene, Tom}},
  issn         = {{0177-0667}},
  journal      = {{ENGINEERING WITH COMPUTERS}},
  keywords     = {{OPTIMIZATION,ALGORITHM,Feasible region,Gaussian process,Adaptive sampling,Stopping criterion}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Adaptive sampling with automatic stopping for feasible region identification in engineering design}},
  url          = {{http://dx.doi.org/10.1007/s00366-021-01341-7}},
  year         = {{2021}},
}

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