  Gaussian process surrogates are a popular alternative to directly using
computationally expensive simulation models. When the simulation output
consists of many responses, dimension-reduction techniques are often employed
to construct these surrogates. However, surrogate methods with dimension
reduction generally rely on complete output training data. This article
proposes a new Gaussian process surrogate method that permits the use of
partially observed output while remaining computationally efficient. The new
method involves the imputation of missing values and the adjustment of the
covariance matrix used for Gaussian process inference. The resulting surrogate
represents the available responses, disregards the missing responses, and
provides meaningful uncertainty quantification. The proposed approach is shown
to offer sharper inference than alternatives in a simulation study and a case
study where an energy density functional model that frequently returns
incomplete output is calibrated.
