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A Compressed Sensing Approach to Uncertainty Propagation for Approximately Additive Functions

[+] Author Affiliations
Kaiyu Li, Douglas Allaire

Texas A&M University, College Station, TX

Paper No. DETC2016-60195, pp. V01AT02A027; 11 pages
doi:10.1115/DETC2016-60195
From:
  • ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 1A: 36th Computers and Information in Engineering Conference
  • Charlotte, North Carolina, USA, August 21–24, 2016
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5007-7
  • Copyright © 2016 by ASME

abstract

Computational models for numerically simulating physical systems are increasingly being used to support decision-making processes in engineering. Processes such as design decisions, policy level analyses, and experimental design settings are often guided by information gained from computational modeling capabilities. To ensure effective application of results obtained through numerical simulation of computational models, uncertainty in model inputs must be propagated to uncertainty in model outputs. For expensive computational models, the many thousands of model evaluations required for traditional Monte Carlo based techniques for uncertainty propagation can be prohibitive. This paper presents a novel methodology for constructing surrogate representations of computational models via compressed sensing. Our approach exploits the approximate additivity inherent in many engineering computational modeling capabilities. We demonstrate our methodology on an analytical function and a cooled gas turbine blade application. The results of these applications reveal substantial computational savings over traditional Monte Carlo simulation with negligible loss of accuracy.

Copyright © 2016 by ASME
Topics: Uncertainty

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