Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911081736175616 |
|---|---|
| author | Lanzoni, Daniele Pierre-Louis, Olivier Bergamaschini, Roberto Montalenti, Francesco |
| author_facet | Lanzoni, Daniele Pierre-Louis, Olivier Bergamaschini, Roberto Montalenti, Francesco |
| contents | We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. Specifically, we showcase the application to a two-dimensional, many-particle system, focusing on surface-step fluctuations and on the related time-dependent roughness. After the construction of a dataset based on Kinetic Monte Carlo simulations, a conditional GAN is trained to propagate stochastically the state of the system in time, allowing the generation of new sequences with a reduced computational cost. Modifications with respect to standard GANs, which facilitate convergence and increase accuracy, are discussed. The trained network is demonstrated to quantitatively reproduce equilibrium and kinetic properties, including scaling laws, with deviations of a few percent from the exact value. Extrapolation limits and future perspectives are critically discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks Lanzoni, Daniele Pierre-Louis, Olivier Bergamaschini, Roberto Montalenti, Francesco Statistical Mechanics Materials Science Artificial Intelligence Machine Learning Computational Physics We show that Generative Adversarial Networks (GANs) may be fruitfully exploited to learn stochastic dynamics, surrogating traditional models while capturing thermal fluctuations. Specifically, we showcase the application to a two-dimensional, many-particle system, focusing on surface-step fluctuations and on the related time-dependent roughness. After the construction of a dataset based on Kinetic Monte Carlo simulations, a conditional GAN is trained to propagate stochastically the state of the system in time, allowing the generation of new sequences with a reduced computational cost. Modifications with respect to standard GANs, which facilitate convergence and increase accuracy, are discussed. The trained network is demonstrated to quantitatively reproduce equilibrium and kinetic properties, including scaling laws, with deviations of a few percent from the exact value. Extrapolation limits and future perspectives are critically discussed. |
| title | Learning Kinetic Monte Carlo stochastic dynamics with Deep Generative Adversarial Networks |
| topic | Statistical Mechanics Materials Science Artificial Intelligence Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2507.21763 |