  In this paper, a fast and parallelizable method based on Gaussian Processes
(GPs) is introduced to emulate computer models that simulate the formation of
binary black holes (BBHs) through the evolution of pairs of massive stars. Two
obstacles that arise in this application are the a priori unknown conditions of
BBH formation and the large scale of the simulation data. We address them by
proposing a local emulator which combines a GP classifier and a GP regression
model. The resulting emulator can also be utilized in planning future computer
simulations through a proposed criterion for sequential design. By propagating
uncertainties of simulation input through the emulator, we are able to obtain
the distribution of BBH properties under the distribution of physical
parameters.
