Publications (17)
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…
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 $…
Variance reduction in lattice QCD observables via normalizing flows
Ryan Abbott, Denis Boyda, Yang Fu +5
Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parame…
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…
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…
Collective Communication for 100k+ GPUs
Min Si, Pavan Balaji, Yongzhou Chen +36
The increasing scale of large language models (LLMs) necessitates highly efficient collective communication frameworks, particularly as training workloads extend to hundreds of tho…
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 flow models to the generation of correlated lattice QCD ensembles
Ryan Abbott, Aleksandar Botev, Denis Boyda +7
Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different act…
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…
Progress in Normalizing Flows for 4d Gauge Theories
Ryan Abbott, Denis Boyda, Daniel C. Hackett +4
Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensio…
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…
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…
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…
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…
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…
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…
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…