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Article
Novel Computational Methods for High-Dimensional Stochastic Sensitivity Analysis
International Journal for Numerical Methods in Engineering (2014)
  • Sharif Rahman, University of Iowa
  • Xuchun Ren, Georgia Southern University
Abstract
This paper presents three new computational methods for calculating design sensitivities of statistical moments and reliability of high-dimensional complex systems subject to random input. The first method represents a novel integration of the polynomial dimensional decomposition (PDD) of a multivariate stochastic response function and score functions. Applied to the statistical moments, the method provides mean-square convergent analytical expressions of design sensitivities of the first two moments of a stochastic response. The second and third methods, relevant to probability distribution or reliability analysis, exploit two distinct combinations built on PDD: the PDD-saddlepoint approximation (SPA) or PDD-SPA method, entailing SPA and score functions; and the PDD-Monte Carlo simulation (MCS) or PDD-MCS method, utilizing the embedded MCS of the PDD approximation and score functions. For all three methods developed, the statistical moments or failure probabilities and their design sensitivities are both determined concurrently from a single stochastic analysis or simulation. Numerical examples, including a 100-dimensional mathematical problem, indicate that the new methods developed provide not only theoretically convergent or accurate design sensitivities, but also computationally efficient solutions. A practical example involving robust design optimization of a three-hole bracket illustrates the usefulness of the proposed methods.
Keywords
  • Dimension reduction,
  • Orthogonal polynomials,
  • Polynomial dimensional decomposition,
  • Robust design optimization,
  • Saddlepoint approximation,
  • Score function
Disciplines
Publication Date
June 22, 2014
DOI
10.1002/nme.4659
Citation Information
Sharif Rahman and Xuchun Ren. "Novel Computational Methods for High-Dimensional Stochastic Sensitivity Analysis" International Journal for Numerical Methods in Engineering Vol. 98 Iss. 12 (2014) p. 881 - 916 ISSN: 1097-0207
Available at: http://works.bepress.com/xuchun_ren/2/