Fast emulation and calibration of large computer experiments with multivariate output

Scientific investigations are often expensive and the ability to quickly perform analysis of data on-location at experimental facilities can save valuable resources. Further, computer models that leverage scientific knowledge can be used to gain insight into complex processes and reduce the need for costly physical experiments, but in turn may be computationally expensive to run. We compare multiple statistical surrogates or emulators based on Gaussian processes for expensive computer models, with the goal of producing predictions quickly given large training sets. We then present a modularised approach for finding the values of inputs that allow for the surrogate model to match reality, or field observations. This process is model calibration. We then extend the emulator of choice and calibration procedure for use with multivariate response and demonstrate the speed and efficacy of such emulators on datasets from a series of transmission impact experiments.