Machine-learning physics from unphysics: Finding deconfinement
temperature in lattice Yang-Mills theories from outside the scaling window

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Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window

23 September 2020

M. N. Chernodub

N. Gerasimeniuk

ArXiv (abs)PDF HTML

Papers citing "Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window"

6 / 6 papers shown

Title
Sampling using $SU(N)$ gauge equivariant flows D. Boyda G. Kanwar S. Racanière Danilo Jimenez Rezende M. S. Albergo Kyle Cranmer D. Hackett P. Shanahan 84 129 0 12 Aug 2020
Extending machine learning classification capabilities with histogram reweighting Dimitrios Bachtis Gert Aarts B. Lucini 50 21 0 29 Apr 2020
Equivariant flow-based sampling for lattice gauge theory G. Kanwar M. S. Albergo D. Boyda Kyle Cranmer D. Hackett S. Racanière Danilo Jimenez Rezende P. Shanahan AI4CE 66 176 0 13 Mar 2020
Flow-based generative models for Markov chain Monte Carlo in lattice field theory M. S. Albergo G. Kanwar P. Shanahan AI4CE 105 219 0 26 Apr 2019
A high-bias, low-variance introduction to Machine Learning for physicists Pankaj Mehta Marin Bukov Ching-Hao Wang A. G. Day C. Richardson Charles K. Fisher D. Schwab AI4CE 113 879 0 23 Mar 2018
Generalizing Hamiltonian Monte Carlo with Neural Networks Daniel Levy Matthew D. Hoffman Jascha Narain Sohl-Dickstein BDL 77 130 0 25 Nov 2017

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