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Found 15 papers, 1 papers with code
Normalizing Flows on Tori and Spheres
Normalizing flows are a powerful tool for building expressive distributions in high dimensions.
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge-invariant by construction.
Sampling using $SU(N)$ gauge equivariant flows
We develop a flow-based sampling algorithm for $SU(N)$ lattice gauge theories that is gauge-invariant by construction.
Introduction to Normalizing Flows for Lattice Field Theory
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.
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine learning approaches to sampling complicated probability distributions in a way that can be made asymptotically exact.
Flow-based sampling for multimodal distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-based generative models are a promising new approach for configuration generation in lattice field theory.
Applications of Machine Learning to Lattice Quantum Field Theory
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.
Flow-based sampling in the lattice Schwinger model at criticality
In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the critical value of the fermion mass.
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampling in fermionic lattice field theories using pseudofermions as stochastic estimators for the fermionic determinant.
Neural-network preconditioners for solving the Dirac equation in lattice gauge theory
This work develops neural-network--based preconditioners to accelerate solution of the Wilson-Dirac normal equation in lattice quantum field theories.
Aspects of scaling and scalability for flow-based sampling of lattice QCD
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 freezing.
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
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.
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics.
Applications of flow models to the generation of correlated lattice QCD ensembles
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 action parameters.
Practical applications of machine-learned flows on gauge fields
Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes.
