  We consider adapting a canonical computer model calibration apparatus,
involving coupled Gaussian process (GP) emulators, to a computer experiment
simulating radiative shock hydrodynamics that is orders of magnitude larger
than what can typically be accommodated. The conventional approach calls for
thousands of large matrix inverses to evaluate the likelihood in an MCMC
scheme. Our approach replaces that costly ideal with a thrifty take on
essential ingredients, synergizing three modern ideas in emulation, calibration
and optimization: local approximate GP regression, modularization, and mesh
adaptive direct search. The new methodology is motivated both by necessity -
considering our particular application - and by recent trends in the
supercomputer simulation literature. A synthetic data application allows us to
explore the merits of several variations in a controlled environment and,
together with results on our motivating real-data experiment, lead to
noteworthy insights into the dynamics of radiative shocks as well as the
limitations of the calibration enterprise generally.
