  Numerical lattice quantum chromodynamics studies of the strong interaction
are important in many aspects of particle and nuclear physics. Such studies
require significant computing resources to undertake. A number of proposed
methods promise improved efficiency of lattice calculations, and access to
regions of parameter space that are currently computationally intractable, via
multi-scale action-matching approaches that necessitate parametric regression
of generated lattice datasets. The applicability of machine learning to this
regression task is investigated, with deep neural networks found to provide an
efficient solution even in cases where approaches such as principal component
analysis fail. The high information content and complex symmetries inherent in
lattice QCD datasets require custom neural network layers to be introduced and
present opportunities for further development.
