research-article

On direct gradient enhanced simulation metamodels

Authors:

Michael C. FuAuthors Info & Claims

WSC '12: Proceedings of the Winter Simulation Conference

Article No.: 43, Pages 1 - 12

Published: 09 December 2012 Publication History

Abstract

Traditional metamodel-based optimization methods assume experiment data collected consist of performance measurements only. However, in many settings found in stochastic simulation, direct gradient estimates are available. We investigate techniques that augment existing regression and stochastic kriging models to incorporate additional gradient information. The augmented models are shown to be compelling compared to existing models, in the sense of improved accuracy or reducing simulation cost. Numerical results also indicate that the augmented models can capture trends that standard models miss.

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Liu, W. 2003. Development of gradient-enhanced kriging approximations for multidisciplinary design optimization. Ph. D. thesis, University of Notre Dame.

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Cited By

Barton R(2015)TutorialProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888818(1765-1779)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888818
Chau MFu MQu HRyzhov IBuckley SMiller J(2014)Simulation optimizationProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693858(21-35)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693858
Qu HFu M(2014)Gradient Extrapolated Stochastic KrigingACM Transactions on Modeling and Computer Simulation10.1145/265899524:4(1-25)Online publication date: 18-Nov-2014
https://dl.acm.org/doi/10.1145/2658995

Index Terms

On direct gradient enhanced simulation metamodels
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation types and techniques

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Information & Contributors

Information

Published In

cover image ACM Conferences

WSC '12: Proceedings of the Winter Simulation Conference

December 2012

4271 pages

General Chair:
Oliver Rose
Universität der Bundeswehr München, Germany
,
Program Chair:
Adelinde M. Uhrmacher
University of Rostock, Germany

Sponsors

IIE: Institute of Industrial Engineers
INFORMS-SIM: Institute for Operations Research and the Management Sciences: Simulation Society
SCS: Shanghai Computer Society
SIGSIM: ACM Special Interest Group on Simulation and Modeling

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Winter Simulation Conference

Publication History

Published: 09 December 2012

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WSC '12

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IIE
INFORMS-SIM
SCS
SIGSIM

WSC '12: Winter Simulation Conference

December 9 - 12, 2012

Berlin, Germany

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WSC '12 Paper Acceptance Rate 189 of 384 submissions, 49%;

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

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Cited By

Barton R(2015)TutorialProceedings of the 2015 Winter Simulation Conference10.5555/2888619.2888818(1765-1779)Online publication date: 6-Dec-2015
https://dl.acm.org/doi/10.5555/2888619.2888818
Chau MFu MQu HRyzhov IBuckley SMiller J(2014)Simulation optimizationProceedings of the 2014 Winter Simulation Conference10.5555/2693848.2693858(21-35)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.5555/2693848.2693858
Qu HFu M(2014)Gradient Extrapolated Stochastic KrigingACM Transactions on Modeling and Computer Simulation10.1145/265899524:4(1-25)Online publication date: 18-Nov-2014
https://dl.acm.org/doi/10.1145/2658995

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