  We investigate the applicability of machine learning techniques in studying
the finite-size effects associated with many-body physics. These techniques
have an emerging presence in many-body theory as they have been used for
interpolations, extrapolations, and in modeling wavefunctions. We will resolve
several issues associated with machine learning and many-body calculations such
as small datasets, outliers, and discontinuities, for the purpose of
extrapolating finite calculations to macroscopic scales. We carry out a
systematic investigation of two related systems by developing metrics that aim
to avoid spurious effects and capture desired features. This work uses neural
networks to extrapolate the Unitary Gas to the thermodynamic limit at
zero-range, which is otherwise difficult to reach. The effective mass of
strongly interacting neutron matter is also studied and makes use of the
non-interacting problem to resolve discontinuous predictions. For this
investigation, we also carried out new Auxiliary Field Diffusion Monte Carlo
(AFDMC) calculations for a variety of densities and particle numbers.
Ultimately, we demonstrate an effective utility for neural networks in this
context.
