13 citations · 18 across the 3 of their papers we have counts for
3 papers · 1 filter
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +10
Recent applications of machine-learned normalizing flows to sampling in lattice field theory suggest that such methods may be able to mitigate critical slowing down and topological…
Applications of Machine Learning to Lattice Quantum Field Theory
Denis Boyda, Salvatore Calì, Sam Foreman +8
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In th…
Applying machine learning methods to prediction problems of lattice observables
N. V. Gerasimeniuk, M. N. Chernodub, V. A. Goy +3
We discuss the prediction of critical behavior of lattice observables in SU(2) and SU(3) gauge theories. We show that feed-forward neural network, trained on the lattice configurat…