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

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arxiv 2311.18312 v1 pith:EL4V6SWH submitted 2023-11-30 cond-mat.str-el hep-lat

Marcel Rodekamp , Evan Berkowitz , Maria Dincă , Christoph Gäntgen , Stefan Krieg , Thomas Luu This is my paper · ORCID
classification cond-mat.str-elhep-lat
keywords systemssignnetworksneuralproblemrealapplicabilityapplied
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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.

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  1. Path optimization method for the sign problem caused by fermion determinant

    hep-lat 2025-02 conditional novelty 5.0 of 10

    Path optimization with machine learning reproduces analytic results in the 1D lattice Thirring model, and dropping the Jacobian from the learning step still works.

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