Multifidelity forward uncertainty quantification (UQ) problems often involve
multiple quantities of interest and heterogeneous models (e.g., different
grids, equations, dimensions, physics, surrogate and reduced-order models).
While computational efficiency is key in this context, multi-output strategies
in multilevel/multifidelity methods are either sub-optimal or non-existent. In
this paper we extend multilevel best linear unbiased estimators (MLBLUE) to
multi-output forward UQ problems and we present new semidefinite programming
formulations for their optimal setup. Not only do these formulations yield the
optimal number of samples required, but also the optimal selection of
low-fidelity models to use. While existing MLBLUE approaches are single-output
only and require a non-trivial nonlinear optimization procedure, the new
multi-output formulations can be solved reliably and efficiently. We
demonstrate the efficacy of the new methods and formulations in practical UQ
problems with model heterogeneity.Comment: 22 pages, 5 figures, 3 table