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Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples in thermodynamic equilibrium.
Time-dependent statistics of the ising model
Roy J Glauber · 1963
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Dependence of critical properties on dimensionality of spins
H. E. Stanley · 1968
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Replica monte carlo simulation of spin-glasses
Robert H Swendsen and Jian-Sheng Wang · 1986
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Hybrid monte carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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Markov chain monte carlo maximum likelihood
Charles J. Geyer · 1991
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Exchange monte carlo method and application to spin glass simulations
K. Hukushima and K. Nemoto · 1996
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Parallel tempering: Theory, applications, and new perspectives
David J. Earl and Michael W. Deem · 2005
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Monte Carlo strategies in scientific computing
Jun S Liu · 2008
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Density estimation by dual ascent of the log-likelihood
Esteban G. Tabak and Eric Vanden-Eijnden · 2010
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Improved side-chain torsion potentials for the amber ff99sb protein force field
Kresten Lindorff-Larsen, Stefano Piana, Kim Palmo, Paul Maragakis, John L. Klepeis, Ron O. Dror, and David E. Shaw · 2010
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Markov models of molecular kinetics: Generation and validation
Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina Keller, Martin Senne, Martin Held, John D Chodera, Christof Schütte, and Frank Noé · 2011
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Identification of slow molecular order parameters for markov model construction
Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé · 2013
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A family of nonparametric density estimation algorithms
Esteban G Tabak and Cristina V Turner · 2013
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Temperature-dependent solvation modulates the dimensions of disordered proteins
René Wuttke, Hagen Hofmann, Daniel Nettels, Madeleine B. Borgia, Jeetain Mittal, Robert B. Best, and Benjamin Schuler · 2014
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Nice: Nonlinear independent components estimation
L. Dinh, D. Krueger, and Y. Bengio · 2015
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simmonett, Matthew P Harrigan, Chaya D Stern, Rafal P Wiewiora, Bernard R Brooks, and Vijay S Pande · 2017
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B-t phase diagram of pd/fe/ir(111) computed with parallel tempering monte carlo
M Böttcher, S Heinze, S Egorov, J Sinova, and B Dupé · 2018
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Neural canonical transformation with symplectic flows
Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, and Lei Wang · 2020
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Stochastic normalizing flows
Hao Wu, Jonas Köhler, and Frank Noé · 2020
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Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli, Shinichi Nakajima, Nils Strodthoff, W. Samek, K. Müller, and P. Kessel · 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 Racaniere, Danilo Jimenez Rezende, and Phiala E Shanahan · 2020
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Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
C. Huang, Laurent Dinh, and Aaron C. Courville · 2020
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Boltzmann generators-sampling equilibrium states of many-body systems with deep learning
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Flow-based generative models for markov chain monte carlo in lattice field theory
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Equivariant flows: exact likelihood generative learning for symmetric densities
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Normalizing flows for probabilistic modeling and inference
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Estimation of thermodynamic observables in lattice field theories with deep generative models
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Flow-based sampling for fermionic lattice field theories
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Deeptime: a python library for machine learning dynamical models from time series data
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