Variational Autoregressive Networks with probability priors
For computational physicists, the work offers a practical improvement to neural-network-based samplers by leveraging physical priors, though it is incremental as it builds on existing strategies.
The paper tackles critical slowing down in Monte Carlo simulations near phase transitions by incorporating physical priors into variational autoregressive networks. Results show reduced training burden and improved simulation of larger system sizes for the Ising and Edwards-Anderson spin glass models.
Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increase in autocorrelation times near phase transitions. Although deep learning approaches, such as neural-network-based samplers, have been proposed to alleviate this issue, they face another serious problem: the difficulty of training the models. This difficulty partially stems from the overly general nature of original machine-learning architectures, which often ignore underlying physical symmetries and force networks to relearn them from scratch. In this paper, we demonstrate that incorporating physical priors into the model significantly enhances performance. Building upon existing strategies that integrate spin-spin interactions, we propose a framework that utilizes a prior probability distribution as a starting point for training. Our results for the Ising model, as well as for the Edwards-Anderson spin glass model, suggest that moving away from `blank slate' models in favor of physics-informed priors reduces the training burden and facilitates the simulation of larger system sizes in discrete spin models.