Related references
Note: Only part of the references are listed.Neural network flows of low q-state Potts and clock models
Dimitrios Giataganas et al.
NEW JOURNAL OF PHYSICS (2022)
Conditional noise deep learning for parameter estimation of gravitational wave events
Han-Shiang Kuo et al.
PHYSICAL REVIEW D (2022)
Machine learning detection of Berezinskii-Kosterlitz-Thouless transitions in q-state clock models
Yusuke Miyajima et al.
PHYSICAL REVIEW B (2021)
Unsupervised machine learning of topological phase transitions from experimental data
Niklas Kaeming et al.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2021)
Deep generative models of gravitational waveforms via conditional autoencoder
Chung-Hao Liao et al.
PHYSICAL REVIEW D (2021)
Drawing Phase Diagrams of Random Quantum Systems by Deep Learning the Wave Functions
Tomi Ohtsuki et al.
JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN (2020)
Machine-Learning Studies on Spin Models
Kenta Shiina et al.
SCIENTIFIC REPORTS (2020)
Entanglement-Entropy Study of Phase Transitions in Six-State Clock Model
R. Krcmar et al.
ACTA PHYSICA POLONICA A (2020)
The critical temperature of the 2D-Ising model through deep learning autoencoders
Constantia Alexandrou et al.
EUROPEAN PHYSICAL JOURNAL B (2020)
Thermodynamics and feature extraction by machine learning
Shotaro Shiba Funai et al.
PHYSICAL REVIEW RESEARCH (2020)
Critical properties of the two-dimensional q-state clock model
Zi-Qian Li et al.
PHYSICAL REVIEW E (2020)
Is Deep Learning a Renormalization Group Flow?
Ellen De Mello Koch et al.
IEEE ACCESS (2020)
Asymptotically unbiased estimation of physical observables with neural samplers
Kim A. Nicoli et al.
PHYSICAL REVIEW E (2020)
Machine-learning study using improved correlation configuration and application to quantum Monte Carlo simulation
Yusuke Tomita et al.
PHYSICAL REVIEW E (2020)
A high-bias, low-variance introduction to Machine Learning for physicists
Pankaj Mehta et al.
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS (2019)
On the Jensen-Shannon Symmetrization of Distances Relying on Abstract Means
Frank Nielsen
ENTROPY (2019)
Machine learning and the physical sciences
Giuseppe Carleo et al.
REVIEWS OF MODERN PHYSICS (2019)
Approaching the Kosterlitz-Thouless transition for the classical XY model with tensor networks
Laurens Vanderstraeten et al.
PHYSICAL REVIEW E (2019)
Neural-network quantum state tomography
Giacomo Torlai et al.
NATURE PHYSICS (2018)
Parameter diagnostics of phases and phase transition learning by neural networks
Philippe Suchsland et al.
PHYSICAL REVIEW B (2018)
Scale-invariant feature extraction of neural network and renormalization group flow
Satoshi Iso et al.
PHYSICAL REVIEW E (2018)
Learning phase transitions from dynamics
Evert van Nieuwenburg et al.
PHYSICAL REVIEW B (2018)
Approximating quantum many-body wave functions using artificial neural networks
Zi Cai et al.
PHYSICAL REVIEW B (2018)
Learning phase transitions by confusion
Evert P. L. van Nieuwenburg et al.
NATURE PHYSICS (2017)
Machine learning phases of matter
Juan Carrasquilla et al.
NATURE PHYSICS (2017)
Quantum Loop Topography for Machine Learning
Yi Zhang et al.
PHYSICAL REVIEW LETTERS (2017)
Machine learning for quantum physics
Michael R. Hush
SCIENCE (2017)
Solving the quantum many-body problem with artificial neural networks
Giuseppe Carleo et al.
SCIENCE (2017)
Machine learning quantum phases of matter beyond the fermion sign problem
Peter Broecker et al.
SCIENTIFIC REPORTS (2017)
Machine learning Z2 quantum spin liquids with quasiparticle statistics
Yi Zhang et al.
PHYSICAL REVIEW B (2017)
Probing many-body localization with neural networks
Frank Schindler et al.
PHYSICAL REVIEW B (2017)
A structural approach to relaxation in glassy liquids
S. S. Schoenholz et al.
NATURE PHYSICS (2016)
DMRG study of the Berezinskii-Kosterlitz-Thouless transitions of the 2D five-state clock model
Christophe Chatelain
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2014)
Searching for exotic particles in high-energy physics with deep learning
P. Baldi et al.
NATURE COMMUNICATIONS (2014)
Dualities and the phase diagram of the p-clock model
G. Ortiz et al.
NUCLEAR PHYSICS B (2012)
Universality away from critical points in two-dimensional phase transitions
CM Lapilli et al.
PHYSICAL REVIEW LETTERS (2006)
Probability-changing cluster algorithm for two-dimensional XY and clock models -: art. no. 184405
Y Tomita et al.
PHYSICAL REVIEW B (2002)
Share Paper
