Hauptseite > Publikationsdatenbank > Machine learning to alleviate Hubbard-model sign problems > print

001     891466
005     20220111142650.0
024 7 _ |a 10.1103/PhysRevB.103.125153
|2 doi
024 7 _ |a 0163-1829
|2 ISSN
024 7 _ |a 0556-2805
|2 ISSN
024 7 _ |a 1095-3795
|2 ISSN
024 7 _ |a 1098-0121
|2 ISSN
024 7 _ |a 1538-4489
|2 ISSN
024 7 _ |a 1550-235X
|2 ISSN
024 7 _ |a 2469-9950
|2 ISSN
024 7 _ |a 2469-9969
|2 ISSN
024 7 _ |a 2469-9977
|2 ISSN
024 7 _ |a 2128/27520
|2 Handle
024 7 _ |a altmetric:102763939
|2 altmetric
024 7 _ |a WOS:000646187300005
|2 WOS
037 _ _ |a FZJ-2021-01548
082 _ _ |a 530
100 1 _ |a Wynen, Jan-Lukas
|0 P:(DE-Juel1)171733
|b 0
|e Corresponding author
245 _ _ |a Machine learning to alleviate Hubbard-model sign problems
260 _ _ |a Woodbury, NY
|c 2021
|b Inst.77671
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1641839633_25112
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Lattice Monte Carlo calculations of interacting systems on non-bipartite lattices exhibit an oscillatory imaginary phase known as the phase or sign problem, even at zero chemical potential. One method to alleviate the sign problem is to analytically continue the integration region of the state variables into the complex plane via holomorphic flow equations. For asymptotically large flow times the state variables approach manifolds of constant imaginary phase known as Lefschetz thimbles. However, flowing such variables and calculating the ensuing Jacobian is a computationally demanding procedure. In this paper we demonstrate that neural networks can be trained to parameterize suitable manifolds for this class of sign problem and drastically reduce the computational cost. We apply our method to the Hubbard model on the triangle and tetrahedron, both of which are non-bipartite. At strong interaction strengths and modest temperatures the tetrahedron suffers from a severe sign problem that cannot be overcome with standard reweighting techniques, while it quickly yields to our method. We benchmark our results with exact calculations and comment on future directions of this work.
536 _ _ |a 5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)
|0 G:(DE-HGF)POF4-5111
|c POF4-511
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Berkowitz, Evan
|0 P:(DE-Juel1)171536
|b 1
700 1 _ |a Krieg, Stefan
|0 P:(DE-Juel1)132171
|b 2
700 1 _ |a Luu, Thomas
|0 P:(DE-Juel1)159481
|b 3
700 1 _ |a Ostmeyer, Johann
|0 P:(DE-HGF)0
|b 4
773 _ _ |a 10.1103/PhysRevB.103.125153
|g Vol. 103, no. 12, p. 125153
|0 PERI:(DE-600)2844160-6
|n 12
|p 125153
|t Physical review / B
|v 103
|y 2021
|x 2469-9969
856 4 _ |u https://arxiv.org/abs/2006.11221
856 4 _ |u https://juser.fz-juelich.de/record/891466/files/PhysRevB.103.125153.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:891466
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)171733
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)132171
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)159481
913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5111
|x 0
913 0 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Computational Science and Mathematical Methods
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1230
|2 StatID
|b Current Contents - Electronics and Telecommunications Collection
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2021-01-28
915 _ _ |a American Physical Society Transfer of Copyright Agreement
|0 LIC:(DE-HGF)APS-112012
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1150
|2 StatID
|b Current Contents - Physical, Chemical and Earth Sciences
|d 2021-01-28
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2021-01-28
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2021-01-28
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2021-01-28
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b PHYS REV B : 2019
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-28
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2021-01-28
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
920 1 _ |0 I:(DE-Juel1)IAS-4-20090406
|k IAS-4
|l Theorie der Starken Wechselwirkung
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a I:(DE-Juel1)IAS-4-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21