  With the advent of the Square Kilometre Array Observatory (SKAO), scientists
will be able to directly observe the Epoch of Reionization by mapping the
distribution of neutral hydrogen at different redshifts. While physically
motivated results can be simulated with radiative transfer codes, these
simulations are computationally expensive and can not readily produce the
required scale and resolution simultaneously. Here we introduce the
Physics-Informed neural Network for reIONization (PINION), which can accurately
and swiftly predict the complete 4-D hydrogen fraction evolution from the
smoothed gas and mass density fields from pre-computed N-body simulation. We
trained PINION on the C$^2$-Ray simulation outputs and a physics constraint on
the reionization chemistry equation is enforced. With only five redshift
snapshots and a propagation mask as a simplistic approximation of the ionizing
photon mean free path, PINION can accurately predict the entire reionization
history between $z=6$ and $12$. We evaluate the accuracy of our predictions by
analysing the dimensionless power spectra and morphology statistics estimations
against C$^2$-Ray results. We show that while the network's predictions are in
good agreement with simulation to redshift $z>7$, the network's accuracy
suffers for $z<7$ primarily due to the oversimplified propagation mask. We
motivate how PINION performance can be drastically improved and potentially
generalized to large-scale simulations.
