A differentiable programming framework for spin models
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910458901954560 |
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| author | Farias, Tiago de Souza Schultz, Vitor Vaz Mombach, José Carlos Merino Maziero, Jonas |
| author_facet | Farias, Tiago de Souza Schultz, Vitor Vaz Mombach, José Carlos Merino Maziero, Jonas |
| contents | We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_01772 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A differentiable programming framework for spin models Farias, Tiago de Souza Schultz, Vitor Vaz Mombach, José Carlos Merino Maziero, Jonas Statistical Mechanics Machine Learning We introduce a novel framework for simulating spin models using differentiable programming, an approach that leverages the advancements in machine learning and computational efficiency. We focus on three distinct spin systems: the Ising model, the Potts model, and the Cellular Potts model, demonstrating the practicality and scalability of our framework in modeling these complex systems. Additionally, this framework allows for the optimization of spin models, which can adjust the parameters of a system by a defined objective function. In order to simulate these models, we adapt the Metropolis-Hastings algorithm to a differentiable programming paradigm, employing batched tensors for simulating spin lattices. This adaptation not only facilitates the integration with existing deep learning tools but also significantly enhances computational speed through parallel processing capabilities, as it can be implemented on different hardware architectures, including GPUs and TPUs. |
| title | A differentiable programming framework for spin models |
| topic | Statistical Mechanics Machine Learning |
| url | https://arxiv.org/abs/2304.01772 |