Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces

Many multivariate functions in engineering models vary primarily along a few directions in the space of input parameters. When these directions correspond to coordinate directions, one may apply global sensitivity measures to determine the most influential parameters. However, these methods perform...

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Bibliographic Details
Published in:SIAM journal on scientific computing 2014-01, Vol.36 (4), p.A1500-A1524
Main Authors: Constantine, Paul G., Dow, Eric, Wang, Qiqi
Format: Article
Language:English
Subjects:
Gaussian
Kriging
Mathematical analysis
Mathematical models
Response surfaces
Sensitivity analysis
Subspaces
Surface chemistry
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Summary:Many multivariate functions in engineering models vary primarily along a few directions in the space of input parameters. When these directions correspond to coordinate directions, one may apply global sensitivity measures to determine the most influential parameters. However, these methods perform poorly when the directions of variability are not aligned with the natural coordinates of the input space. We present a method to first detect the directions of the strongest variability using evaluations of the gradient and subsequently exploit these directions to construct a response surface on a low-dimensional subspace---i.e., the active subspace ---of the inputs. We develop a theoretical framework with error bounds, and we link the theoretical quantities to the parameters of a kriging response surface on the active subspace. We apply the method to an elliptic PDE model with coefficients parameterized by 100 Gaussian random variables and compare it with a local sensitivity analysis method for dimension reduction.
ISSN:1064-8275
1095-7197
DOI:10.1137/130916138