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001008810 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-02484
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001008810 1001_ $$0P:(DE-Juel1)185942$$aRodekamp, Marcel$$b0$$eCorresponding author$$ufzj
001008810 1112_ $$aThe 39th International Symposium on Lattice Field Theory$$cBonn$$wGermany
001008810 245__ $$aMitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
001008810 260__ $$aTrieste$$bSISSA$$c2022
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001008810 520__ $$aMany fascinating systems suffer from a severe (complex action) sign problem preventing us from calculating them with Markov Chain Monte Carlo simulations. One promising method to alleviate the sign problem is the transformation of the integration domain towards Lefschetz Thimbles. Unfortunately, this suffers from poor scaling originating in numerically integrating of flow equations and evaluation of an induced Jacobian. In this proceedings we present a new preliminary Neural Network architecture based on complex-valued affine coupling layers. This network performs such a transformation efficiently, ultimately allowing simulation of systems with a severe sign problem. We test this method within the Hubbard Model at finite chemical potential, modelling strongly correlated electrons on a spatial lattice of ions.
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001008810 536__ $$0G:(GEPRIS)196253076$$aDFG project 196253076 - TRR 110: Symmetrien und Strukturbildung in der Quantenchromodynamik (196253076)$$c196253076$$x1
001008810 536__ $$0G:(DE-Juel-1)PF-JARA-SDS005$$aSDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005)$$cPF-JARA-SDS005$$x2
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001008810 650_7 $$2Other$$aStrongly Correlated Electrons (cond-mat.str-el)
001008810 650_7 $$2Other$$aHigh Energy Physics - Lattice (hep-lat)
001008810 650_7 $$2Other$$aFOS: Physical sciences
001008810 7001_ $$0P:(DE-Juel1)165594$$aGäntgen, Christoph$$b1$$eCorresponding author$$ufzj
001008810 773__ $$0PERI:(DE-600)2642026-0$$a10.22323/1.430.0032$$p032$$tProceedings of Science / International School for Advanced Studies$$vLATTICE2022$$x1824-8039$$y2022
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001008810 9141_ $$y2023
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