Equivariant flow-based sampling for lattice gauge theory G Kanwar, MS Albergo, D Boyda, K Cranmer, DC Hackett, S Racaniere, ...
Physical Review Letters 125 (12), 121601, 2020
221 2020 Sampling using D Boyda, G Kanwar, S Racanière, DJ Rezende, MS Albergo, K Cranmer, ...
Physical Review D 103 (7), 074504, 2021
151 2021 Interacting electrons in graphene: Fermi velocity renormalization and optical response T Stauber, P Parida, M Trushin, MV Ulybyshev, DL Boyda, J Schliemann
Physical review letters 118 (26), 266801, 2017
80 2017 Flow-based sampling for fermionic lattice field theories MS Albergo, G Kanwar, S Racanière, DJ Rezende, JM Urban, D Boyda, ...
Physical Review D 104 (11), 114507, 2021
56 2021 Sign problem in finite density lattice QCD VA Goy, V Bornyakov, D Boyda, A Molochkov, A Nakamura, A Nikolaev, ...
Progress of Theoretical and Experimental Physics 2017 (3), 031D01, 2017
44 2017 New approach to canonical partition functions computation in VG Bornyakov, DL Boyda, VA Goy, AV Molochkov, A Nakamura, ...
Physical Review D 95 (9), 094506, 2017
43 2017 Many-body effects on graphene conductivity: Quantum Monte Carlo calculations DL Boyda, VV Braguta, MI Katsnelson, MV Ulybyshev
Physical Review B 94 (8), 085421, 2016
42 2016 Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions R Abbott, MS Albergo, D Boyda, K Cranmer, DC Hackett, G Kanwar, ...
Physical Review D 106 (7), 074506, 2022
36 2022 Introduction to normalizing flows for lattice field theory MS Albergo, D Boyda, DC Hackett, G Kanwar, K Cranmer, S Racaniere, ...
arXiv preprint arXiv:2101.08176, 2021
33 2021 Flow-based sampling for multimodal distributions in lattice field theory DC Hackett, CC Hsieh, MS Albergo, D Boyda, JW Chen, KF Chen, ...
arXiv preprint arXiv:2107.00734, 2021
32 2021 Lee-Yang zeros in lattice QCD for searching phase transition points M Wakayama, VG Bornyakov, DL Boyda, VA Goy, H Iida, AV Molochkov, ...
Physics Letters B 793, 227-233, 2019
30 2019 Flow-based sampling in the lattice Schwinger model at criticality MS Albergo, D Boyda, K Cranmer, DC Hackett, G Kanwar, S Racanière, ...
Physical Review D 106 (1), 014514, 2022
28 2022 Applications of machine learning to lattice quantum field theory D Boyda, S Calì, S Foreman, L Funcke, DC Hackett, Y Lin, G Aarts, ...
arXiv preprint arXiv:2202.05838, 2022
26 2022 Applications of flow models to the generation of correlated lattice QCD ensembles R Abbott, A Botev, D Boyda, DC Hackett, G Kanwar, S Racanière, ...
Physical Review D 109 (9), 094514, 2024
24 2024 Finding the deconfinement temperature in lattice Yang-Mills theories from outside the scaling window with machine learning DL Boyda, MN Chernodub, NV Gerasimeniuk, VA Goy, SD Liubimov, ...
Physical Review D 103 (1), 014509, 2021
23 2021 Numerical simulation of graphene in an external magnetic field DL Boyda, VV Braguta, SN Valgushev, MI Polikarpov, MV Ulybyshev
Physical Review B 89 (24), 245404, 2014
19 2014 Aspects of scaling and scalability for flow-based sampling of lattice QCD R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
The European Physical Journal A 59 (11), 257, 2023
18 2023 Sampling QCD field configurations with gauge-equivariant flow models R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
Sissa Medialab, 2022
18 2022 Novel approach to deriving the canonical generating functional in lattice QCD at a finite chemical potential DL Boyda, VG Bornyakov, VA Goy, VI Zakharov, AV Molochkov, ...
JETP letters 104, 657-661, 2016
16 2016 Normalizing flows for lattice gauge theory in arbitrary space-time dimension R Abbott, MS Albergo, A Botev, D Boyda, K Cranmer, DC Hackett, ...
arXiv preprint arXiv:2305.02402, 2023
15 2023