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We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.
Quantum fields on a lattice
István Montvay and Gernot Münster · 1997
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Simulation of φ 4 \varphi^{4} theory in the strong coupling expansion beyond the Ising limit
I. Vierhaus · 2010
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Monte Carlo statistical methods
Christian Robert and George Casella · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Density estimation using real nvp, 2017
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Machine learning at the energy and intensity frontiers of particle physics
Alexander Radovic, Mike Williams, David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao, and Taritree Wongjirad · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P Kingma and Prafulla Dhariwal · 2018
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Neural ordinary differential equations
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová · 2019
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Flow-based generative models for markov chain monte carlo in lattice field theory
MS Albergo, G Kanwar, and PE Shanahan · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi · 2020
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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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Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks
Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T Barron, and Ren Ng · 2020
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Provably efficient machine learning for quantum many-body problems, 2021
Hsin-Yuan Huang, Richard Kueng, Giacomo Torlai, Victor V. Albert, and John Preskill · 2021
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Flow-based sampling for fermionic lattice field theories, 2021
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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Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
Kim A. Nicoli, Christopher J. Anders, Lena Funcke, Tobias Hartung, Karl Jansen, Pan Kessel, Shinichi Nakajima, and Paolo Stornati · 2021
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Efficient modelling of trivializing maps for lattice ϕ 4 \phi^{4} theory using normalizing flows: A first look at scalability, 2021
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Jan M. Pawlowski and Julian M. Urban · 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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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
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Luigi Del Debbio, Joe Marsh Rossney, and Michael Wilson · 2021
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Gauge covariant neural network for 4 dimensional non-abelian gauge theory, 2021
Akio Tomiya and Yuki Nagai · 2021
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Flow-based sampling for multimodal distributions in lattice field theory, 2021
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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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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