222 citations · 961 across the 42 of their papers we have counts for
14 papers · 1 filter
A Monte Carlo estimator of flow fields for sampling and noise problems
Michael S. Albergo, Gurtej Kanwar
Learned field transformations may help address ubiquitous critical slowing down and signal-to-noise problems in lattice field theory. In the context of an annealed sequence of dist…
Practical applications of machine-learned flows on gauge fields
Ryan Abbott, Michael S. Albergo, Denis Boyda +5
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasin…
Multiscale Normalizing Flows for Gauge Theories
Ryan Abbott, Michael S. Albergo, Denis Boyda +5
Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to…
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We repo…
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…
Sampling QCD field configurations with gauge-equivariant flow models
Ryan Abbott, Michael S. Albergo, Aleksandar Botev +11
Machine learning methods based on normalizing flows have been shown to address important challenges, such as critical slowing-down and topological freezing, in the sampling of gaug…
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
Ryan Abbott, Michael S. Albergo, Denis Boyda +9
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic deter…
Flow-based sampling in the lattice Schwinger model at criticality
Michael S. Albergo, Denis Boyda, Kyle Cranmer +7
Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodyna…
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…
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…
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…
Sampling using gauge equivariant flows
Denis Boyda, Gurtej Kanwar, Sébastien Racanière +5
We develop a flow-based sampling algorithm for lattice gauge theories that is gauge-invariant by construction. Our key contribution is constructing a class of flows on an $…
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…
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…