Contribution to a conference proceedings/Journal Article

FZJ-2023-02484

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning

Rodekamp, M. (Corresponding author)FZJ* ; Gäntgen, C. (Corresponding author)FZJ*

2022
SISSA Trieste

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Please use a persistent id in citations: doi:10.22323/1.430.0032  doi:10.34734/FZJ-2023-02484

Abstract: 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.

Keyword(s): Strongly Correlated Electrons (cond-mat.str-el) ; High Energy Physics - Lattice (hep-lat) ; FOS: Physical sciences

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)
2. Theorie der Starken Wechselwirkung (IAS-4)

Research Program(s):

1. 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511) (POF4-511)
2. DFG project 196253076 - TRR 110: Symmetrien und Strukturbildung in der Quantenchromodynamik (196253076) (196253076)
3. SDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005) (PF-JARA-SDS005)

Appears in the scientific report 2023

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Database coverage:
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