  The intrinsic alignments (IA) of galaxies, regarded as a contaminant in weak
lensing analyses, represents the correlation of galaxy shapes due to
gravitational tidal interactions and galaxy formation processes. As such,
understanding IA is paramount for accurate cosmological inferences from weak
lensing surveys; however, one limitation to our understanding and mitigation of
IA is expensive simulation-based modeling. In this work, we present a deep
learning approach to emulate galaxy position-position ($\xi$),
position-orientation ($\omega$), and orientation-orientation ($\eta$)
correlation function measurements and uncertainties from halo occupation
distribution-based mock galaxy catalogs. We find strong Pearson correlation
values with the model across all three correlation functions and further
predict aleatoric uncertainties through a mean-variance estimation training
procedure. $\xi(r)$ predictions are generally accurate to $\leq10\%$. Our model
also successfully captures the underlying signal of the noisier correlations
$\omega(r)$ and $\eta(r)$, although with a lower average accuracy. We find that
the model performance is inhibited by the stochasticity of the data, and will
benefit from correlations averaged over multiple data realizations. Our code
will be made open source upon journal publication.
