  Gravitational wave science is dependent upon expensive numerical simulations,
which provide the foundational understanding of binary merger radiation needed
to interpret observations of massive binary black holes. The high cost of these
simulations limits large-scale campaigns to explore the binary black hole
parameter space. Surrogate models have been developed to efficiently
interpolate between simulation results, but these models require a sufficiently
comprehensive sample to train on. Acquisition functions can be used to identify
points in the domain for simulation. We develop a new acquisition function
which accounts for the cost of simulating new points. We show that when applied
to a 3D domain of binary mass ratio and dimensionless spins, the accumulated
cost of simulation is reduced by a factor of about 10.
