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

In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

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 conformal bootstrap of 1D CFTs on the straight Maldacena–Wilson line in 4D
N= 4 super-Yang–Mills theory. We introduce an improved truncation scheme with an “OPE …

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 …

Both the path integral measure in field theory (FT) and ensembles of neural networks (NN)
describe distributions over functions. When the central limit theorem can be applied in the …

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 …

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 …

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 …

Unraveling the connections between microscopic structure, emergent physical properties,
and slow dynamics has long been a challenge in the field of the glass transition. The …