222 citations · 1.1k across the 42 of their papers we have counts for
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Flow-based sampling for multimodal and extended-mode distributions in lattice field theory
Daniel C. Hackett, Chung-Chun Hsieh, Sahil Pontula +7
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory. In t…
Machine Learning and Variational Algorithms for Lattice Field Theory
Gurtej Kanwar
In lattice quantum field theory studies, parameters defining the lattice theory must be tuned toward criticality to access continuum physics. Commonly used Markov chain Monte Carlo…
Flow-based sampling for fermionic lattice field theories
Michael S. Albergo, Gurtej Kanwar, Sébastien Racanière +6
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotical…
Real-time lattice gauge theory actions: unitarity, convergence, and path integral contour deformations
Gurtej Kanwar, Michael L. Wagman
The Wilson action for Euclidean lattice gauge theory defines a positive-definite transfer matrix that corresponds to a unitary lattice gauge theory time-evolution operator if analy…
Path integral contour deformations for observables in gauge theory
William Detmold, Gurtej Kanwar, Henry Lamm +2
Path integral contour deformations have been shown to mitigate sign and signal-to-noise problems associated with phase fluctuations in lattice field theories. We define a family of…
Introduction to Normalizing Flows for Lattice Field Theory
Michael S. Albergo, Denis Boyda, Daniel C. Hackett +5
This notebook tutorial demonstrates a method for sampling Boltzmann distributions of lattice field theories using a class of machine learning models known as normalizing flows. The…