  We consider large-scale industrial computer model calibration, combining
multi-output simulation with limited physical observation, involved in the
development of a honeycomb seal. Toward that end, we adopt a localized sampling
and emulation strategy called "on-site surrogates (OSSs)", designed to cope
with the amalgamated challenges of high-dimensional inputs, large-scale
simulation campaigns, and nonstationary response surfaces. In previous
applications, OSSs were one-at-a-time affairs for multiple outputs. We
demonstrate that this leads to dissonance in calibration efforts for a common
parameter set across outputs for the honeycomb. Instead, a conceptually
straightforward, but implementationally intricate, principal-components
representation, adapted from ordinary Gaussian process surrogate modeling to
the OSS setting, can resolve this tension. With a two-pronged -
optimization-based and fully Bayesian - approach, we show how pooled
information across outputs can reduce uncertainty and enhance (statistical and
computational) efficiency in calibrated parameters for the honeycomb relative
to the previous, "data-poor" univariate analog.
