Salvato in:
| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2404.09046 |
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Sommario:
- The parameterization method (PM) provides a broad theoretical and numerical foundation for computing invariant manifolds of dynamical systems. PM implements a change of variables in order to represent trajectories of a system of ordinary differential equations ``as simply as possible." In this paper we pursue a similar goal for stochastic oscillator systems. For planar nonlinear stochastic systems that are ``robustly oscillatory", we find a change of variables through which the dynamics are as simple as possible $\textit{in the mean}$. We prove existence and uniqueness of a deterministic vector field, the trajectories of which capture the local mean behavior of the stochastic oscillator. We illustrate the construction of such an ``effective vector field" for several examples, including a limit cycle oscillator perturbed by noise, an excitable system derived from a spiking neuron model, and a spiral sink with noise forcing (2D Ornstein-Uhlenbeck process). The latter examples comprise contingent oscillators that would not sustain rhythmic activity without noise forcing. Finally, we exploit the simplicity of the dynamics after the change of variables to obtain the effective diffusion constant of the resulting phase variable, and the stationary variance of the resulting amplitude (isostable) variable.