Soft amorphous materials are viscoelastic solids ubiquitously found around us, from clays
and cementitious pastes to emulsions and physical gels encountered in food or biomedical …

Many problems in the physical sciences, machine learning, and statistical inference
necessitate sampling from a high-dimensional, multimodal probability distribution. Markov …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Many important challenges in science and technology can be cast as optimization problems.
When viewed in a statistical physics framework, these can be tackled by simulated …

Several strategies have been recently proposed in order to improve Monte Carlo sampling
efficiency using machine learning tools. Here, we challenge these methods by considering a …

Learning to sample from intractable distributions over discrete sets without relying on
corresponding training data is a central problem in a wide range of fields, including …

Normalizing flows can transform a simple prior probability distribution into a more complex
target distribution. Here, we evaluate the ability and efficiency of generative machine …

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

Efficient sampling and approximation of Boltzmann distributions involving large sets of
binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent …

Efficient sampling of complex high-dimensional probability distributions is a central task in
computational science. Machine learning methods like autoregressive neural networks …