Density-dependent stochastic resetting: a large deviations framework for achieving target distributions over networks
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911011045376000 |
|---|---|
| author | Coghi, Francesco Olsen, Kristian Stølevik |
| author_facet | Coghi, Francesco Olsen, Kristian Stølevik |
| contents | We develop a framework for designing density-dependent stochastic resetting protocols to regulate distributions of random walkers on networks. Resetting mechanisms that depend on local densities induce correlations in otherwise non-interacting walkers. Our framework allows for the study of both transient trajectories and stationary properties and identifies resetting protocols that maximise the likelihood of homogeneous and, more generally, rare configurations of random walkers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Density-dependent stochastic resetting: a large deviations framework for achieving target distributions over networks Coghi, Francesco Olsen, Kristian Stølevik Statistical Mechanics Disordered Systems and Neural Networks We develop a framework for designing density-dependent stochastic resetting protocols to regulate distributions of random walkers on networks. Resetting mechanisms that depend on local densities induce correlations in otherwise non-interacting walkers. Our framework allows for the study of both transient trajectories and stationary properties and identifies resetting protocols that maximise the likelihood of homogeneous and, more generally, rare configurations of random walkers. |
| title | Density-dependent stochastic resetting: a large deviations framework for achieving target distributions over networks |
| topic | Statistical Mechanics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2412.10016 |