200 citations · 892 across the 32 of their papers we have counts for
29 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Machine-learning physics from unphysics: Finding deconfinement temperature in lattice Yang-Mills theories from outside the scaling window
D. L. Boyda, M. N. Chernodub, N. V. Gerasimeniuk +3
We study the machine learning techniques applied to the lattice gauge theory's critical behavior, particularly to the confinement/deconfinement phase transition in the SU(2) and SU…
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…
New way of collision experiment data analysis based on Grand Canonical Distribution and Lattice QCD data
V. Bornyakov, D. Boyda, V. Goy +2
We propose new way of heavy ion collisions experiment data analysis. We analyze physical parameters of fireball created in RHIC experiment based on Grand Canonical Distribution and…
Lee-Yang zeros in lattice QCD for searching phase transition points
M. Wakayama, V. G. Bornyakov, D. L. Boyda +5
We report Lee-Yang zeros behavior at finite temperature and density. The quark number densities, <n>, are calculated at the pure imaginary chemical potential, where no sign problem…
Lattice QCD at finite baryon density using analytic continuation
V. G. Bornyakov, D. L. Boyda, V. A. Goy +6
We simulate lattice QCD with two flavors of Wilson fermions at imaginary baryon chemical potential. Results for the baryon number density computed in the confining and deconfining…
Restoring canonical partition functions from imaginary chemical potential
V. G. Bornyakov, D. Boyda, V. Goy +4
Using GPGPU techniques and multi-precision calculation we developed the code to study QCD phase transition line in the canonical approach. The canonical approach is a powerful tool…
Lattice QCD thermodynamics at finite chemical potential and its comparison with Experiments
D. Boyda, V. G. Bornyakov, V. Goy +4
We compare higher moments of baryon numbers measured at the RHIC heavy ion collision experiments with those by the lattice QCD calculations. We employ the canonical approach, in wh…
Sign problem in finite density lattice QCD
V. A. Goy, V. Bornyakov, D. Boyda +4
The canonical approach, which was developed for solving the sign problem, may suffer from a new type of sign problem. In the canonical approach, the grand partition function is wri…