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The numerical sign problem poses a seemingly insurmountable barrier to thesimulation of many fascinating systems. We apply neural networks to deform theregion of integration, mitigating the sign problem of systems with stronglycorrelated electrons. In this talk we present our latest architecturaldevelopments as applied to contour deformation. We also demonstrate itsapplicability to real systems, namely perylene.

From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems