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037 _ _ |a FZJ-2023-02484
082 _ _ |a 530
100 1 _ |a Rodekamp, Marcel
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111 2 _ |a The 39th International Symposium on Lattice Field Theory
|c Bonn
|w Germany
245 _ _ |a Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
260 _ _ |a Trieste
|c 2022
|b SISSA
336 7 _ |a article
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520 _ _ |a 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.
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536 _ _ |a DFG project 196253076 - TRR 110: Symmetrien und Strukturbildung in der Quantenchromodynamik (196253076)
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650 _ 7 |a Strongly Correlated Electrons (cond-mat.str-el)
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650 _ 7 |a High Energy Physics - Lattice (hep-lat)
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650 _ 7 |a FOS: Physical sciences
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700 1 _ |a Gäntgen, Christoph
|0 P:(DE-Juel1)165594
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773 _ _ |a 10.22323/1.430.0032
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|t Proceedings of Science / International School for Advanced Studies
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856 4 _ |u https://juser.fz-juelich.de/record/1008810/files/FZJ-2023-02484_1008810.pdf
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914 1 _ |y 2023
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
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