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Making the Most Out of Surrogate Models: Tricks of the Trade

[+] Author Affiliations
Felipe A. C. Viana, Raphael T. Haftka

University of Florida, Gainesville, FL

Christian Gogu

Université Toulouse III, Toulouse, France

Paper No. DETC2010-28813, pp. 587-598; 12 pages
doi:10.1115/DETC2010-28813
From:
  • ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1: 36th Design Automation Conference, Parts A and B
  • Montreal, Quebec, Canada, August 15–18, 2010
  • Conference Sponsors: Design Engineering Division and Computers in Engineering Division
  • ISBN: 978-0-7918-4409-0 | eISBN: 978-0-7918-3881-5
  • Copyright © 2010 by ASME

abstract

Design analysis and optimization based on high-fidelity computer experiments is commonly expensive. Surrogate modeling is often the tool of choice for reducing the computational burden. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes advanced and yet simple statistical tools that help. We focus on four techniques with increasing popularity in the design automation community: (i) screening and variable reduction in both the input and the output spaces, (ii) simultaneous use of multiple surrogates, (iii) sequential sampling and optimization, and (iv) conservative estimators.

Copyright © 2010 by ASME

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