  Neutron cross section matrices for fission and scattering data are required
for each material, temperature, and enrichment level to calculate the neutron
transport equation accurately. This information can be a limiting factor when
using the multigroup discrete ordinates (SN) method when the number of energy
groups is large. Machine Learning (ML) can be used to replace the need for the
cross section matrices by reproducing the function that maps the scalar flux to
the scattering and fission sources. Through the use of autoencoders and Deep
Jointly-Informed Neural Networks (DJINN), the data storage requirements are
reduced by 94% of the original data for a 618 group problem. This is
accomplished while preserving the scalar flux, maintaining generality, and
decreasing wall clock times.
