Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Molecular dynamics (MD) enables the study of physical systems with excellent
spatiotemporal resolution but suffers from severe timescale limitations. To address this …

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 …

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular
processes on time and length scales inaccessible to all-atom simulations. Parametrizing CG …

We tackle the problem of sampling from intractable high-dimensional density functions, a
fundamental task that often appears in machine learning and statistics. We extend recent …

Active matter systems, from self-propelled colloids to motile bacteria, are characterized by
the conversion of free energy into useful work at the microscopic scale. They involve physics …

This work presents gauge-equivariant architectures for flow-based sampling in fermionic
lattice field theories using pseudofermions as stochastic estimators for the fermionic …

Normalizing flows (NF) are a class of powerful generative models that have gained
popularity in recent years due to their ability to model complex distributions with high …

We present a lightweight, flexible, and high-performance framework for inferring the
properties of gravitational-wave events. By combining likelihood heterodyning, automatically …

Normalizing flows are tractable density models that can approximate complicated target
distributions, eg Boltzmann distributions of physical systems. However, current methods for …