Discovering mean residence time and escape probability from data of stochastic dynamical systems

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.

10 pages, 34 figures, chaos

References in corpus (1)

• Learning interpretable continuous-time models of latent stochastic dynamical systems

Cited by in corpus (5)

• A Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noise
• Detecting the maximum likelihood transition path from data of stochastic dynamic systems
• Discovering transition phenomena from data of stochastic dynamical systems with Lévy noise
• Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
• Detecting Stochastic Governing Laws with Observation on Stationary Distributions