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 …

The integration of deep learning techniques and physics-driven designs is reforming the
way we address inverse problems, in which accurate physical properties are extracted from …

We develop diffusion models for lattice gauge theories which build on the concept of
stochastic quantization. This framework is applied to $ U (1) $ gauge theory in $1+ 1 …

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 …

This study delves into the connection between machine learning and lattice field theory by
linking generative diffusion models (DMs) with stochastic quantization, from a stochastic …

To analyse how diffusion models learn correlations beyond Gaussian ones, we study the
behaviour of higher-order cumulants, or connected n-point functions, under both the forward …

Restricted Boltzmann machines (RBMs) are well-known tools used in machine learning to
learn probability distribution functions from data. We analyze RBMs with scalar fields on the …

We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the
DNNLikelihood proposed in [Eur. Phys. J. C 80, 664 (2020)]. We show, through realistic …

Non-equilibrium Monte Carlo simulations based on Jarzynski's equality are a well-
understood method to compute differences in free energy and also to sample from a target …

Recent work shows that path gradient estimators for normalizing flows have lower variance
compared to standard estimators for variational inference, resulting in improved training …