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The cost of Monte Carlo sampling of lattice configurations is very high in the critical region of lattice field theory due to the high correlation between the samples.
Hybrid monte carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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CRITICAL SLOWING DOWN
Ulli Wolff · 1990
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Simulation of phi 4 theory in the strong coupling expansion beyond the ising limit
Ingmar Vierhaus · 2010
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Critical slowing down and error analysis in lattice QCD simulations
Stefan Schaefer, Rainer Sommer, and Francesco Virotta · 2011
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Playing with the kinetic term in the HMC
Alberto Ramos · 2012
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Improved sampling algorithms in lattice qcd, 2015
Arjun Singh Gambhir and Kostas Orginos · 2015
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Multiscale monte carlo equilibration: Pure yang-mills theory
Michael G. Endres, Richard C. Brower, William Detmold, Kostas Orginos, and Andrew V. Pochinsky · 2015
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Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Learning thermodynamics with boltzmann machines
Giacomo Torlai and Roger G. Melko · 2016
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Discovering phase transitions with unsupervised learning
Lei Wang · 2016
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Self-learning monte carlo method and cumulative update in fermion systems
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Solving the quantum many-body problem with artificial neural networks
G. Carleo and M. Troyer · 2017
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Symmetry-enforced self-learning monte carlo method applied to the holstein model
Chuang Chen, Xiao Yan Xu, Junwei Liu, George Batrouni, Richard Scalettar, and Zi Yang Meng · 2018
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Machine learning topological invariants with neural networks
Pengfei Zhang, Huitao Shen, and Hui Zhai · 2018
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Machine learning action parameters in lattice quantum chromodynamics
Phiala E Shanahan, Daniel Trewartha, and William Detmold · 2018
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Regressive and generative neural networks for scalar field theory
Kai Zhou, Gergely Endrődi, Long-Gang Pang, and Horst Stöcker · 2019
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Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, Wojciech Samek, Klaus-Robert Müller, and Pan Kessel · 2020
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Machine learning for quantum matter
Juan Carrasquilla · 2020
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Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan · 2020
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Generative neural samplers for the quantum heisenberg chain
Johanna Vielhaben and Nils Strodthoff · 2021
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Conditional generative models for sampling and phase transition indication in spin systems
Japneet Singh, Mathias Scheurer, and Vipul Arora · 2021
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Generative learning for the problem of critical slowing down in lattice Gross Neveu model
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Flow-based generative models for markov chain monte carlo in lattice field theory
M. S. Albergo, G. Kanwar, and P. E. Shanahan · 2019
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Guided image generation with conditional invertible neural networks, 2019
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
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Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and Ullrich Köthe · 2019
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Reducing autocorrelation times in lattice simulations with generative adversarial networks
Jan M Pawlowski and Julian M Urban · 2020
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Ankur Singha, Dipankar Chakrabarti, and Vipul Arora · 2021
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Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Julian M. Urban, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, and Phiala E. Shanahan · 2021
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Introduction to normalizing flows for lattice field theory, 2021
Michael S. Albergo, Denis Boyda, Daniel C. Hackett, Gurtej Kanwar, Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan · 2021
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Flow-based sampling for multimodal distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Michael S. Albergo, Denis Boyda, Jiunn-Wei Chen, Kai-Feng Chen, Kyle Cranmer, Gurtej Kanwar, and Phiala E. Shanahan · 2021
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Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Gurtej Kanwar, Sébastien Racanière, Danilo J. Rezende, Fernando Romero-López, Phiala E. Shanahan, and Julian M. Urban · 2022
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