  Gravitational waves in the postmerger phase of binary neutron star mergers
may become detectable with planned upgrades of existing gravitational-wave
detectors or with more sensitive next-generation detectors. The construction of
template banks for the postmerger phase can facilitate signal detection and
parameter estimation. Here, we investigate the performance of an artificial
neural network in predicting simulation-based waveforms in the frequency domain
(restricted to the magnitude of the frequency spectrum and to equal-mass
models) that depend on three parameters that can be inferred through
observations, neutron star mass, tidal deformability, and the gradient of
radius versus mass. Compared to a baseline study using multiple linear
regression, we find that the artificial neural network can predict waveforms
with higher accuracy and more consistent performance in a cross-validation
study. We also demonstrate, through a recalibration procedure, that future
reduction of uncertainties in empirical relations that are used in our
hierarchical scheme will result in more accurate predicted postmerger spectra.
