  Semi-numerical simulations are the leading candidates for evolving
reionization on cosmological scales. These semi-numerical models are efficient
in generating large-scale maps of the 21cm signal, but they are too slow to
enable inference at the field level. We present different strategies to train a
U-Net to accelerate these simulations. We derive the ionization field directly
from the initial density field without using the ionizing sources' location,
and hence emulating the radiative transfer process. We find that the U-Net
achieves higher accuracy in reconstructing the ionization field if the input
includes either white noise or a noisy version of the ionization map beside the
density field during training. Our model reconstructs the power spectrum over
all scales perfectly well. This work represents a step towards generating
large-scale ionization maps with a minimal cost and hence enabling rapid
parameter inference at the field level.
