Fast sequential computer model calibration of large nonstationary spatial-temporal processes

Computer models enable scientists to investigate real-world phenomena in a virtual laboratory using computer experiments. Statistical calibration enables scientists to incorporate field data in this analysis. However, the practical application is hardly straightforward for data structures such as spatial-temporal fields, which are usually large or not well represented by a stationary process model. We present a computationally efficient approach to estimating the calibration parameters using a criterion that measures discrepancy between the computer model output and field data. One can then construct empirical distributions for the calibration parameters and propose new computer model trials using sequential design. The approach is relatively simple to implement using existing algorithms and is able to estimate calibration parameters for large and nonstationary data. Supplementary R code is available online.