Active subspace methods in theory and practice: applications to kriging surfaces

TL;DR

This paper introduces a method to identify key input directions in high-dimensional functions using gradients, enabling efficient construction of response surfaces like kriging on low-dimensional active subspaces, with theoretical error bounds and practical applications.

Contribution

It develops a theoretical framework for active subspace detection using gradients and links it to kriging response surfaces, improving dimension reduction in complex models.

Findings

Effective detection of active subspaces using gradient evaluations.

Successful application to a PDE model with 100 parameters.

Comparison shows advantages over local sensitivity methods.

Abstract

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.