  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.
