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 …

General-purpose Markov chain Monte Carlo sampling algorithms suffer from a dramatic
reduction in efficiency as the system being studied is driven toward a critical point through …

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 …

Scope 1.1. Research questions to be answered from measurement data require algorithms.
A substantial part of physics research relies on algorithms that scientists develop as static …

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 …

The recent introduction of machine learning techniques, especially normalizing flows, for the
sampling of lattice gauge theories has shed some hope on improving the sampling …

Lattice gauge equivariant convolutional neural networks (L-CNNs) are a framework for
convolutional neural networks that can be applied to non-Abelian lattice gauge theories …

We study the inverse problem of reconstructing spectral functions from Euclidean correlation
functions via machine learning. We propose a novel neural network, SVAE, which is based …

Quantum-Chromo dynamics (QCD) is a fundamental theory for quarks and gluons, which
describes both the sub-atomic world and the history of our universe. The simulation for QCD …

We introduce a training objective for continuous normalizing flows that can be used in the
absence of samples but in the presence of an energy function. Our method relies on either a …