Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

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 …

This paper introduces a generative model equivariant to Euclidean symmetries: E (n)
Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the discriminative E (n) …

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

Despite the great promise of quantum machine learning models, there are several
challenges one must overcome before unlocking their full potential. For instance, models …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

A bstract Deep learning methods have been increasingly adopted to study jets in particle
physics. Since symmetry-preserving behavior has been shown to be an important factor for …

There has been enormous progress in the last few years in designing neural networks that
respect the fundamental symmetries and coordinate freedoms of physical law. Some of …

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 …

Although seemingly disparate, high-energy nuclear physics (HENP) and machine learning
(ML) have begun to merge in the last few years, yielding interesting results. It is worthy to …