Adaptively Sampling via Regional Variance-Based Sensitivities

TL;DR

This paper introduces an adaptive sampling method based on regional variance sensitivities to improve global sensitivity analysis, demonstrated on both low and high-dimensional examples including industrial bioproduct production.

Contribution

It develops a novel local total sensitivity index and an adaptive sampling strategy that leverages regional sensitivities for efficient exploration of multivariate functions.

Findings

Effective on nonlinear three-dimensional example

Successful application to high-dimensional industrial simulation

Enhanced understanding of variable contributions to variance

Abstract

Inspired by the well-established variance-based methods for global sensitivity analysis, we develop a local total sensitivity index that decomposes the global total sensitivity conditions by independent variables' values. We employ this local sensitivity index in a new method of experimental design that sequentially and adaptively samples the domain of a multivariate function according to local contributions to the global variance. The method is demonstrated on a nonlinear illustrative example that has a three-dimensional domain and a three-dimensional codomain, but also on a complex, high-dimensional simulation for assessing the industrial viability of the production of bioproducts from biomass.