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Simulation Optimization for Computer Models With Multivariate Output

[+] Author Affiliations
Raed Kontar, Shiyu Zhou

University of Wisconsin-Madison, Madison, WI

John Horst

National Institute of Standards and Technology, Gaithersburg, MD

Paper No. MSEC2017-2907, pp. V004T05A029; 8 pages
doi:10.1115/MSEC2017-2907
From:
  • ASME 2017 12th International Manufacturing Science and Engineering Conference collocated with the JSME/ASME 2017 6th International Conference on Materials and Processing
  • Volume 4: Bio and Sustainable Manufacturing
  • Los Angeles, California, USA, June 4–8, 2017
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5075-6
  • Copyright © 2017 by ASME

abstract

This paper explores the potential of Gaussian process based Metamodels for simulation optimization with multivariate outputs. Specifically we focus on Multivariate Gaussian process models established through separable and non-separable covariance structures. We discuss the advantages and drawbacks of each approach and their potential applicability in manufacturing systems. The advantageous features of the Multivariate Gaussian process models are then demonstrated in a case study for the optimization of manufacturing performance metrics.

Copyright © 2017 by ASME

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