  Simulation models of critical systems often have parameters that need to be
calibrated using observed data. For expensive simulation models, calibration is
done using an emulator of the simulation model built on simulation output at
different parameter settings. Using intelligent and adaptive selection of
parameters to build the emulator can drastically improve the efficiency of the
calibration process. The article proposes a sequential framework with a novel
criterion for parameter selection that targets learning the posterior density
of the parameters. The emergent behavior from this criterion is that
exploration happens by selecting parameters in uncertain posterior regions
while simultaneously exploitation happens by selecting parameters in regions of
high posterior density. The advantages of the proposed method are illustrated
using several simulation experiments and a nuclear physics reaction model.

Methodology