Noise-aware neural network for stochastic dynamics simulation
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917614174863360 |
|---|---|
| author | Wu, Pei-Fang Guo, Wei-Chen He, Liang |
| author_facet | Wu, Pei-Fang Guo, Wei-Chen He, Liang |
| contents | In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investigate these phenomena, numerical approaches for simulating stochastic dynamics are indispensable and can be computationally expensive. In light of the recent fast development in machine learning techniques, here, we establish a generic machine learning approach to simulate the stochastic dynamics, dubbed the noise-aware neural network (NANN). One key feature of this approach is its ability to generate the long-time stochastic dynamics of complex large-scale systems by just training NANN with the one-step dynamics of smaller-scale systems, thus reducing the computational cost. Furthermore, this NANN based approach is quite generic. Case-by-case special design of the architecture of NANN is not necessary when it is employed to investigate different stochastic complex systems. Using the noisy Kuramoto model and the Vicsek model as concrete examples, we demonstrate its capability in simulating stochastic dynamics. We believe that this novel machine learning approach can be a useful tool in investigating the large spatio-temporal scaling behavior of complex systems subjected to the influences of the environmental noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_09370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Noise-aware neural network for stochastic dynamics simulation Wu, Pei-Fang Guo, Wei-Chen He, Liang Statistical Mechanics Computational Physics In the presence of system-environment coupling, classical complex systems undergo stochastic dynamics, where rich phenomena can emerge at large spatio-temporal scales. To investigate these phenomena, numerical approaches for simulating stochastic dynamics are indispensable and can be computationally expensive. In light of the recent fast development in machine learning techniques, here, we establish a generic machine learning approach to simulate the stochastic dynamics, dubbed the noise-aware neural network (NANN). One key feature of this approach is its ability to generate the long-time stochastic dynamics of complex large-scale systems by just training NANN with the one-step dynamics of smaller-scale systems, thus reducing the computational cost. Furthermore, this NANN based approach is quite generic. Case-by-case special design of the architecture of NANN is not necessary when it is employed to investigate different stochastic complex systems. Using the noisy Kuramoto model and the Vicsek model as concrete examples, we demonstrate its capability in simulating stochastic dynamics. We believe that this novel machine learning approach can be a useful tool in investigating the large spatio-temporal scaling behavior of complex systems subjected to the influences of the environmental noise. |
| title | Noise-aware neural network for stochastic dynamics simulation |
| topic | Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2403.09370 |