  Rapidly growing catalogs of compact binary mergers from advanced
gravitational-wave detectors allow us to explore the astrophysics of massive
stellar binaries. Merger observations can constrain the uncertain parameters
that describe the underlying processes in the evolution of stars and binary
systems in population models. In this paper, we demonstrate that binary black
hole populations - namely, detection rates, chirp masses, and redshifts - can
be used to measure cosmological parameters describing the redshift-dependent
star formation rate and metallicity distribution. We present a method that uses
artificial neural networks to emulate binary population synthesis computer
models, and construct a fast, flexible, parallelisable surrogate model that we
use for inference.
