A Very Effective and Simple Diffusion Reconstruction for the Diluted Ising Model

Abstract

Diffusion-based generative models are machine learning models that use diffusion processes to learn the probability distribution of high-dimensional data. In recent years, they have become extremely successful in generating multimedia content. However, it is still unknown if such models can be used to generate high-quality datasets of physical models. In this work, we use a Landau-Ginzburg-like diffusion model to infer the distribution of a $2D$ bond-diluted Ising model. Our approach is simple and effective, and we show that the generated samples reproduce correctly the statistical and critical properties of the physical model.

References in corpus (5)

• Inverse statistical problems: from the inverse Ising problem to data science
• Synthetic Lagrangian Turbulence by Generative Diffusion Models
• Dynamical Regimes of Diffusion Models
• Generative diffusion in very large dimensions
• Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective

Cited by in corpus (3)

• Deep learning of phase transitions with minimal examples
• Performance of machine-learning-assisted Monte Carlo in sampling from simple statistical physics models
• Phase probabilities in first-order transitions using machine learning