Quantum Chromodynamics, the theory of quarks and gluons, whose interactions can be
described by a local SU (3) gauge symmetry with charges called “color quantum numbers” …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

We propose a unifying approach that starts from the perturbative construction of trivializing
maps by Lüscher and then improves on it by learning. The resulting continuous normalizing …

We propose a novel machine learning method for sampling from the high-dimensional
probability distributions of Lattice Field Theories, which is based on a single neural ODE …

Sampling topological quantities in the Monte Carlo simulation of lattice gauge theory
becomes challenging as we approach the continuum limit (a→ 0) of the theory. In this work …

At fine lattice spacings, Markov chain Monte Carlo simulations of QCD and other gauge
theories with or without fermions are plagued by slow modes that give rise to large …

We apply the hierarchical autoregressive neural network sampling algorithm to the two-
dimensional Q-state Potts model and perform simulations around the phase transition at Q …

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 …

We demonstrate that gauge-equivariant pooling and unpooling layers can perform as well
as traditional restriction and prolongation layers in multigrid preconditioner models for lattice …

We describe a direct method to estimate the bipartite mutual information of a classical spin
system based on Monte Carlo sampling enhanced by autoregressive neural networks. It …