  The numerical sign problem poses a seemingly insurmountable barrier to the
simulation of many fascinating systems. We apply neural networks to deform the
region of integration, mitigating the sign problem of systems with strongly
correlated electrons. In this talk we present our latest architectural
developments as applied to contour deformation. We also demonstrate its
applicability to real systems, namely perylene.
