2 citations · 2 across the 1 of their papers we have counts for
7 papers
Equivariant flow-based sampling for lattice gauge theory
Gurtej Kanwar, Michael S. Albergo, Denis Boyda +5
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction. We demonstrate the application of this fram…
Path integral contour deformations for noisy observables
William Detmold, Gurtej Kanwar, Michael L. Wagman +1
Monte Carlo studies of many quantum systems face exponentially severe signal-to-noise problems. We show that noise arising from complex phase fluctuations of observables can be red…
Normalizing Flows on Tori and Spheres
Danilo Jimenez Rezende, George Papamakarios, Sébastien Racanière +4
Normalizing flows are a powerful tool for building expressive distributions in high dimensions. So far, most of the literature has concentrated on learning flows on Euclidean space…
Lattice Analysis of with 1 Adjoint Dirac Flavor
Zhen Bi, Anthony Grebe, Gurtej Kanwar +3
Recently Yang-Mills theory with one massless adjoint Dirac quark flavor emerges as a novel critical theory that can describe the evolution between a trivial insulator and a…
Flow-based generative models for Markov chain Monte Carlo in lattice field theory
M. S. Albergo, G. Kanwar, P. E. Shanahan
A Markov chain update scheme using a machine-learned flow-based generative model is proposed for Monte Carlo sampling in lattice field theories. The generative model may be optimiz…
Unwrapping phase fluctuations in one dimension
William Detmold, Gurtej Kanwar, Michael L. Wagman
Correlation functions in one-dimensional complex scalar field theory provide a toy model for phase fluctuations, sign problems, and signal-to-noise problems in lattice field theory…