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

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 …

We study the consequences of mode-collapse of normalizing flows in the context of lattice
field theory. Normalizing flows allow for independent sampling. For this reason, it is hoped …

We compute the nucleon axial and pseudoscalar form factors using three N f= 2+ 1+ 1
twisted-mass fermion ensembles with all quark masses tuned to approximately their physical …

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 …

A bstract In this work, we establish a direct connection between generative diffusion models
(DMs) and stochastic quantization (SQ). The DM is realized by approximating the reversal of …

We demonstrate that a state-of-the-art multigrid preconditioner can be learned efficiently by
gauge-equivariant neural networks. We show that the models require minimal retraining on …

Fixed point lattice actions are designed to have continuum classical properties unaffected by
discretization effects and reduced lattice artifacts at the quantum level. They provide a …

Critical slowing down and topological freezing severely hinder Monte Carlo sampling of
lattice field theories as the continuum limit is approached. Recently, significant progress has …

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 …