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.
Volume 453 -
The 40th International Symposium on Lattice Field Theory (LATTICE2023) -
Algorithms and Artificial Intelligence
From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems
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Pre-published on:
May 05, 2024
Published on:
November 06, 2024
Abstract
DOI: https://doi.org/10.22323/1.453.0031
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Copyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.