Transferability of Winning Lottery Tickets in Neural Network
  Differential Equation Solvers
Edward Prideaux-Ghee
Recent work has shown that renormalisation group theory is a useful framework
with which to describe the process of pruning neural networks via iterative
magnitude pruning. This report formally describes the link between RG theory
and IMP and extends previous results around the Lottery Ticket Hypothesis and
Elastic Lottery Hypothesis to Hamiltonian Neural Networks for solving
differential equations. We find lottery tickets for two Hamiltonian Neural
Networks and demonstrate transferability between the two systems, with accuracy
being dependent on integration times. The universality of the two systems is
then analysed using tools from an RG perspective.