A Very Effective and Simple Diffusion Reconstruction for the Diluted Ising Model
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916607415025664 |
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| author | Bae, Stefano Marinari, Enzo Ricci-Tersenghi, Federico |
| author_facet | Bae, Stefano Marinari, Enzo Ricci-Tersenghi, Federico |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Very Effective and Simple Diffusion Reconstruction for the Diluted Ising Model Bae, Stefano Marinari, Enzo Ricci-Tersenghi, Federico Disordered Systems and Neural Networks 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. |
| title | A Very Effective and Simple Diffusion Reconstruction for the Diluted Ising Model |
| topic | Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2407.07266 |