TY  - JOUR
AU  - Rodekamp, Marcel
AU  - Gäntgen, Christoph
TI  - Mitigating the Hubbard Sign Problem. A Novel Application of Machine Learning
JO  - Proceedings of Science / International School for Advanced Studies
VL  - LATTICE2022
SN  - 1824-8039
CY  - Trieste
PB  - SISSA
M1  - FZJ-2023-02484
SP  - 032
PY  - 2022
AB  - 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.
T2  - The 39th International Symposium on Lattice Field Theory
CY  - , Bonn (Germany)
M2  - Bonn, Germany
KW  - Strongly Correlated Electrons (cond-mat.str-el) (Other)
KW  - High Energy Physics - Lattice (hep-lat) (Other)
KW  - FOS: Physical sciences (Other)
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)16
DO  - DOI:10.22323/1.430.0032
UR  - https://juser.fz-juelich.de/record/1008810
ER  -