From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems
Strongly Correlated Electrons
2023-12-01 v1 High Energy Physics - Lattice
Abstract
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.
Cite
@article{arxiv.2311.18312,
title = {From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems},
author = {Marcel Rodekamp and Evan Berkowitz and Maria Dincă and Christoph Gäntgen and Stefan Krieg and Thomas Luu},
journal= {arXiv preprint arXiv:2311.18312},
year = {2023}
}