Issue |
EPL
Volume 129, Number 4, February 2020
|
|
---|---|---|
Article Number | 40003 | |
Number of page(s) | 6 | |
Section | General | |
DOI | https://doi.org/10.1209/0295-5075/129/40003 | |
Published online | 17 March 2020 |
Precursors to rare events in stochastic resonance
1 Nordita, Royal Institute of Technology and Stockholm University - Stockholm 106 91, Sweden
2 Department of Mathematics, Stockholm University - Stockholm 106 91, Sweden
3 Yale University - New Haven, CT 06520, USA
Received: 28 November 2019
Accepted: 18 February 2020
In stochastic resonance, a periodically forced Brownian particle in a double-well potential jumps between minima at rare increments, the prediction of which poses a major theoretical challenge. Here, we use a path-integral method to find a precursor to these transitions by determining the most probable (or “optimal”) space-time path of a particle. We characterize the optimal path using a direct comparison principle between the Langevin and Hamiltonian dynamical descriptions, allowing us to express the jump condition in terms of the accumulation of noise around the stable periodic path. In consequence, as a system approaches a rare event these fluctuations approach one of the deterministic minimizers, thereby providing a precursor for predicting a stochastic transition. We demonstrate the method numerically, which allows us to determine whether a state is following a stable periodic path or will experience an incipient jump with a high probability. The vast range of systems that exhibit stochastic resonance behavior insures broad relevance of our framework, which allows one to extract precursor fluctuations from data.
PACS: 05.40.Jc – Brownian motion / 05.40.Ca – Noise / 02.50.Ey – Stochastic processes
© EPLA, 2020
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.