Discovering mean residence time and escape probability from data of stochastic dynamical systems
Dynamical Systems
2019-10-02 v1
Abstract
We present a method to learn mean residence time and escape probability from data modeled by stochastic differential equations. This method is a combination of machine learning from data (to extract stochastic differential equations as models) and stochastic dynamics (to quantify dynamical behaviors with deterministic tools). The goal is to learn and understand stochastic dynamics based on data. This method is applicable to sample path data collected from complex systems, as long as these systems can be modeled as stochastic differential equations.
Keywords
Cite
@article{arxiv.1909.00901,
title = {Discovering mean residence time and escape probability from data of stochastic dynamical systems},
author = {Dengfeng Wu and Miaomiao Fu and Jinqiao Duan},
journal= {arXiv preprint arXiv:1909.00901},
year = {2019}
}
Comments
10 pages, 34 figures, chaos