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09/03/2023
Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics
Sampling from known probability distributions is a ubiquitous task in co...
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05/03/2023
Normalizing flows for lattice gauge theory in arbitrary space-time dimension
Applications of normalizing flows to the sampling of field configuration...
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11/14/2022
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Recent applications of machine-learned normalizing flows to sampling in ...
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08/04/2022
Neural-network preconditioners for solving the Dirac equation in lattice gauge theory
This work develops neural-network–based preconditioners to accelerate so...
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07/18/2022
Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions
This work presents gauge-equivariant architectures for flow-based sampli...
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02/23/2022
Flow-based sampling in the lattice Schwinger model at criticality
Recent results suggest that flow-based algorithms may provide efficient ...
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02/10/2022
Applications of Machine Learning to Lattice Quantum Field Theory
There is great potential to apply machine learning in the area of numeri...
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07/01/2021
Flow-based sampling for multimodal distributions in lattice field theory
Recent results have demonstrated that samplers constructed with flow-bas...
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06/10/2021
Flow-based sampling for fermionic lattice field theories
Algorithms based on normalizing flows are emerging as promising machine ...
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01/20/2021
Introduction to Normalizing Flows for Lattice Field Theory
This notebook tutorial demonstrates a method for sampling Boltzmann dist...
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08/12/2020
Sampling using SU(N) gauge equivariant flows
We develop a flow-based sampling algorithm for SU(N) lattice gauge theor...
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03/13/2020
Equivariant flow-based sampling for lattice gauge theory
We define a class of machine-learned flow-based sampling algorithms for ...
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02/06/2020
