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 whether 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 two-dimensional bond-diluted Ising model. Our approach is simple and effective, and we show that the generated samples correctly reproduce the statistical and critical properties of the physical model.

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• Artificial neural networks
• Disordered systems
• Ising model
• Langevin equation