  This work develops a novel basis-adaptive method for constructing anisotropic
polynomial chaos expansions of multidimensional (vector-valued, multi-output)
model responses. The adaptive basis selection is based on multivariate
sensitivity analysis metrics that can be estimated by post-processing the
polynomial chaos expansion and results in a common anisotropic polynomial basis
for the vector-valued response. This allows the application of the method to
problems with up to moderately high-dimensional model inputs (in the order of
tens) and up to very high-dimensional model responses (in the order of
thousands). The method is applied to different engineering test cases for
surrogate modeling and uncertainty quantification, including use cases related
to electric machine and power grid modeling and simulation, and is found to
produce highly accurate results with comparatively low data and computational
demand.
