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Media type:
E-Article
Title:
Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
Contributor:
Rodekamp, Marcel
[Author];
Gäntgen, Christoph
[Author]
Published:
SISSA, 2022
Published in:Proceedings of Science / International School for Advanced Studies LATTICE2022, 032 (2022). doi:10.22323/1.430.0032 ; The 39th International Symposium on Lattice Field Theory, Bonn, Germany
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
Many fascinating systems suffer from a severe (complex action) sign problem preventing us from calculating them with Markov Chain Monte Carlo simulations. One promising method to alleviate the sign problem is the transformation of the integration domain towards Lefschetz Thimbles. Unfortunately, this suffers from poor scaling originating in numerically integrating of flow equations and evaluation of an induced Jacobian. In this proceedings we present a new preliminary Neural Network architecture based on complex-valued affine coupling layers. This network performs such a transformation efficiently, ultimately allowing simulation of systems with a severe sign problem. We test this method within the Hubbard Model at finite chemical potential, modelling strongly correlated electrons on a spatial lattice of ions.