- Home
- Publications
- Publication Search
- Publication Details
Title
Stochastic normalizing flows as non-equilibrium transformations
Authors
Keywords
-
Journal
JOURNAL OF HIGH ENERGY PHYSICS
Volume 2022, Issue 7, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-07-05
DOI
10.1007/jhep07(2022)015
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Lattice Gauge Equivariant Convolutional Neural Networks
- (2022) Matteo Favoni et al. PHYSICAL REVIEW LETTERS
- Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
- (2021) Kim A. Nicoli et al. PHYSICAL REVIEW LETTERS
- Detecting gravitational waves from cosmological phase transitions with LISA: an update
- (2020) Chiara Caprini et al. JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS
- Strong coupling from non-equilibrium Monte Carlo simulations
- (2020) Olmo Francesconi et al. JOURNAL OF HIGH ENERGY PHYSICS
- Equivariant Flow-Based Sampling for Lattice Gauge Theory
- (2020) Gurtej Kanwar et al. PHYSICAL REVIEW LETTERS
- Classifying Topological Charge in SU(3) Yang-Mills Theory with Machine Learning
- (2020) Takuya Matsumoto et al. Progress of Theoretical and Experimental Physics
- Normalizing Flows: An Introduction and Review of Current Methods
- (2020) Ivan Kobyzev et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- QCD-aware recursive neural networks for jet physics
- (2019) Gilles Louppe et al. JOURNAL OF HIGH ENERGY PHYSICS
- Thermodynamics for pure SU(2) gauge theory using gradient flow
- (2019) T Hirakida et al. Progress of Theoretical and Experimental Physics
- Thermodynamics in quenched QCD: energy–momentum tensor with two-loop order coefficients in the gradient-flow formalism
- (2019) Takumi Iritani et al. Progress of Theoretical and Experimental Physics
- Machine Learning as a universal tool for quantitative investigations of phase transitions
- (2019) Cinzia Giannetti et al. NUCLEAR PHYSICS B
- Status and future perspectives for lattice gauge theory calculations to the exascale and beyond
- (2019) Bálint Joó et al. EUROPEAN PHYSICAL JOURNAL A
- Machine learning and the physical sciences
- (2019) Giuseppe Carleo et al. REVIEWS OF MODERN PHYSICS
- Thermalization and prethermalization in isolated quantum systems: a theoretical overview
- (2018) Takashi Mori et al. JOURNAL OF PHYSICS B-ATOMIC MOLECULAR AND OPTICAL PHYSICS
- Machine learning at the energy and intensity frontiers of particle physics
- (2018) Alexander Radovic et al. NATURE
- Deep Learning and Its Application to LHC Physics
- (2018) Dan Guest et al. Annual Review of Nuclear and Particle Science
- Neural Network Renormalization Group
- (2018) Shuo-Hui Li et al. PHYSICAL REVIEW LETTERS
- Equation of state of the SU(3) Yang–Mills theory: A precise determination from a moving frame
- (2017) Leonardo Giusti et al. PHYSICS LETTERS B
- From quantum chaos and eigenstate thermalization to statistical mechanics and thermodynamics
- (2016) Luca D'Alessio et al. ADVANCES IN PHYSICS
- Parton distributions for the LHC run II
- (2015) Richard D. Ball et al. JOURNAL OF HIGH ENERGY PHYSICS
- A neural network clustering algorithm for the ATLAS silicon pixel detector
- (2014) The ATLAS collaboration Journal of Instrumentation
- Density of states approach to dense quantum systems
- (2014) Kurt Langfeld et al. PHYSICAL REVIEW D
- Density estimation by dual ascent of the log-likelihood
- (2013) Esteban G. Tabak et al. Communications in Mathematical Sciences
- Chiral and deconfinement aspects of the QCD transition
- (2012) A. Bazavov et al. PHYSICAL REVIEW D
- Density of States in Gauge Theories
- (2012) Kurt Langfeld et al. PHYSICAL REVIEW LETTERS
- Escorted free energy simulations
- (2011) Suriyanarayanan Vaikuntanathan et al. JOURNAL OF CHEMICAL PHYSICS
- Nonequilibrium fluctuations, fluctuation theorems, and counting statistics in quantum systems
- (2009) Massimiliano Esposito et al. REVIEWS OF MODERN PHYSICS
- Fluctuation–dissipation: Response theory in statistical physics
- (2008) U MARCONI et al. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started