  There is growing interest in the detection and characterization of
gravitational waves from postmerger oscillations of binary neutron stars. These
signals contain information about the nature of the remnant and the
high-density and out-of-equilibrium physics of the postmerger processes, which
would complement any electromagnetic signal. However, the construction of
binary neutron star postmerger waveforms is much more complicated than for
binary black holes: (i) there are theoretical uncertainties in the neutron-star
equation of state and other aspects of the high-density physics, (ii) numerical
simulations are expensive and available ones only cover a small fraction of the
parameter space with limited numerical accuracy, and (iii) it is unclear how to
parametrize the theoretical uncertainties and interpolate across parameter
space. In this work, we describe the use of a machine-learning method called a
conditional variational autoencoder (CVAE) to construct postmerger models for
hyper/massive neutron star remnant signals based on numerical-relativity
simulations. The CVAE provides a probabilistic model, which encodes
uncertainties in the training data within a set of latent parameters. We
estimate that training such a model will ultimately require $\sim 10^4$
waveforms. However, using synthetic training waveforms as a proof-of-principle,
we show that the CVAE can be used as an accurate generative model and that it
encodes the equation of state in a useful latent representation.
