  Gravitational wave observations from merging compact objects are becoming
commonplace, and as detectors improve and gravitational wave sources become
more varied, it is increasingly important to have dense and expansive template
banks of predicted gravitational waveforms. Since numerical relativity is the
only way to fully solve the non-linear merger regime of general relativity for
comparably massed systems, numerical relativity simulations are critical for
gravitational wave detection and analysis. These simulations are
computationally expensive, with each simulation placing one point within the
high dimensional parameter space of binary black hole coalescences. This makes
it important to have a method of placing new simulations in ways that use our
computational resources optimally while ensuring sufficient coverage of the
parameter space. Accomplishing this requires predicting the impact of a new set
of parameters before performing the simulation. To this effect, this paper
introduces a neural network to predict the mismatch between the gravitational
waves of two binary systems. Using this network, we then show how we can
propose new numerical relativity simulations that will provide the most
benefit. We also use the network to identify gaps in existing public catalogs
and identify degeneracies in the binary black hole parameter space.
