Many fascinating systems suffer from a severe (complex action) sign problem preventing us
from calculating them with Markov Chain Monte Carlo simulations. One promising method
to alleviate the sign problem is the transformation of the integration domain towards Lefschetz
Thimbles. Unfortunately, this suffers from poor scaling originating in numerically integrating
of flow equations and evaluation of an induced Jacobian. In this proceedings we present a new
preliminary Neural Network architecture based on complex-valued affine coupling layers. This
network performs such a transformation efficiently, ultimately allowing simulation of systems with
a severe sign problem. We test this method within the Hubbard Model at finite chemical potential,
modelling strongly correlated electrons on a spatial lattice of ions.