Fetching the paper…
Reading the bibliography…
GomalizingFlow.jl: is a package to generate configurations for quantum field theory on the lattice using the flow based sampling algorithm in Julia programming language.
M. S. Albergo, G. Kanwar, P. E. Shanahan, Flow-based generative models for Markov chain Monte Carlo in lattice field theory, Phys. Rev. D 100 (3) (2019) 034515 · 1904
Earlier work this paper cites.
doi:10.1016/0370-2693(80)90138-0
H. Nicolai, On a New Characterization of Scalar Supersymmetric Theories, Phys. Lett. B 89 (1980) 341 · 1980
Earlier work this paper cites.
doi:10.1016/0370-2693(87)91197-X
S. Duane, A. D. Kennedy, B. J. Pendleton, D. Roweth, Hybrid Monte Carlo, Phys. Lett. B 195 (1987) 216–222 · 1987
Earlier work this paper cites.
arXiv:hep-lat/9810063
S. Capitani, M. Lüscher, R. Sommer, H. Wittig, Non-perturbative quark mass renormalization in quenched lattice QCD, Nucl. Phys. B 544 (1999) 669–698, [Erratum: Nucl.Phys.B 582, 762–762 (2000)] · 2000
Earlier work this paper cites.
D. E. Groom, et al., Review of particle physics. Particle Data Group, Eur. Phys. J. C 15 (2000) 1–878
2000
Earlier work this paper cites.
arXiv:hep-lat/0101001
R. Frezzotti, P. A. Grassi, S. Sint, P. Weisz, Lattice QCD with a chirally twisted mass term, JHEP 08 (2001) 058 · 2001
Earlier work this paper cites.
G. Kanwar, M. S. Albergo, D. Boyda, K. Cranmer, D. C. Hackett, S. Racanière, D. J. Rezende, P. E. Shanahan, Equivariant flow-based sampling for lattice gauge theory, Phys. Rev. Lett. 125 (12) (2020) 121601 · 2003
Earlier work this paper cites.
arXiv:hep-lat/0409106
M. Luscher, Schwarz-preconditioned HMC algorithm for two-flavour lattice QCD, Comput. Phys. Commun. 165 (2005) 199–220 · 2004
Earlier work this paper cites.
arXiv:hep-lat/0608015
M. A. Clark, A. D. Kennedy, Accelerating dynamical fermion computations using the rational hybrid Monte Carlo (RHMC) algorithm with multiple pseudofermion fields, Phys. Rev. Lett. 98 (2007) 051601 · 2007
Earlier work this paper cites.
S. Durr, et al., Ab-Initio Determination of Light Hadron Masses, Science 322 (2008) 1224–1227 · 2008
Earlier work this paper cites.
D. Boyda, G. Kanwar, S. Racanière, D. J. Rezende, M. S. Albergo, K. Cranmer, D. C. Hackett, P. E. Shanahan, Sampling using S U ( N ) SU(N) gauge equivariant flows, Phys. Rev. D 103 (7) (2021) 074504 · 2008
Earlier work this paper cites.
J. contributors, Julia Micro-Benchmarks, https://julialang.org/benchmarks/ , [Online; accessed 18-August-2022] (2008)
2008
Cited alongside, same era.
doi:10.1007/978-3-642-01850-3
C. Gattringer, C. B. Lang, Quantum chromodynamics on the lattice, Vol. 788, Springer, Berlin, 2010 · 2010
Cited alongside, same era.
S. Borsanyi, G. Endrodi, Z. Fodor, A. Jakovac, S. D. Katz, S. Krieg, C. Ratti, K. K. Szabo, The QCD equation of state with dynamical quarks, JHEP 11 (2010) 077 · 2010
Cited alongside, same era.
S. Schaefer, R. Sommer, F. Virotta, Critical slowing down and error analysis in lattice QCD simulations, Nucl. Phys. B 845 (2011) 93–119 · 2010
Cited alongside, same era.
M. Luscher, Trivializing maps, the Wilson flow and the HMC algorithm, Commun. Math. Phys. 293 (2010) 899–919 · 2010
Y. Aoki, et al., FLAG Review 2021 (11 2021) · 2021
Later among the works it cites.
A. Tomiya, Y. Nagai, Gauge covariant neural network for 4 dimensional non-abelian gauge theory (3 2021) · 2021
Later among the works it cites.
M. S. Albergo, G. Kanwar, S. Racanière, D. J. Rezende, J. M. Urban, D. Boyda, K. Cranmer, D. C. Hackett, P. E. Shanahan, Flow-based sampling for fermionic lattice field theories, Phys. Rev. D 104 (11) (2021) 114507 · 2021
Later among the works it cites.
M. S. Albergo, D. Boyda, D. C. Hackett, G. Kanwar, K. Cranmer, S. Racanière, D. J. Rezende, P. E. Shanahan, Introduction to Normalizing Flows for Lattice Field Theory (1 2021) · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
J. Bezanson, S. Karpinski, V. B. Shah, A. Edelman, Julia: A fast dynamic language for technical computing (2012) · 2012
Cited alongside, same era.
A. Bazavov, et al., Equation of state in ( 2+1 )-flavor QCD, Phys. Rev. D 90 (2014) 094503 · 2014
Cited alongside, same era.
A. Tanaka, A. Tomiya, Towards reduction of autocorrelation in HMC by machine learning (12 2017) · 2017
Cited alongside, same era.
T. Aoyama, et al., The anomalous magnetic moment of the muon in the Standard Model, Phys. Rept. 887 (2020) 1–166 · 2020
Cited alongside, same era.
J. M. Pawlowski, J. M. Urban, Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks, Mach. Learn. Sci. Tech. 1 (2020) 045011 · 2020
Cited alongside, same era.
doi:10.22323/1.372.0007
K. Zhou, G. Endrodi, L.-G. Pang, H. Stöcker, Generative Model Study for 1+1d-Complex Scalar Field Theory, PoS AISIS2019 (2020) 007 · 2020
Cited alongside, same era.
L. Del Debbio, J. M. Rossney, M. Wilson, Efficient modeling of trivializing maps for lattice ϕ \phi 4 theory using normalizing flows: A first look at scalability, Phys. Rev. D 104 (9) (2021) 094507 · 2021
Later among the works it cites.
D. Boyda, et al., Applications of Machine Learning to Lattice Quantum Field Theory, in: 2022 Snowmass Summer Study, 2022 · 2022
Closest in time.
S. Foreman, X.-Y. Jin, J. C. Osborn, LeapfrogLayers: A Trainable Framework for Effective Topological Sampling, PoS LATTICE2021 (2022) 508 · 2022
Closest in time.
R. Abbott, et al., Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions (7 2022) · 2022
Closest in time.
R. Abbott, et al., Sampling QCD field configurations with gauge-equivariant flow models, in: 39th International Symposium on Lattice Field Theory, 2022 · 2022
Closest in time.
S. Foreman, T. Izubuchi, L. Jin, X.-Y. Jin, J. C. Osborn, A. Tomiya, HMC with Normalizing Flows, PoS LATTICE2021 (2022) 073 · 2022
Closest in time.
M. Rodekamp, E. Berkowitz, C. Gäntgen, S. Krieg, T. Luu, J. Ostmeyer, Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks (3 2022) · 2022
Closest in time.