Discovering transition phenomena from data of stochastic dynamical systems with Lévy noise

Abstract

It is a challenging issue to analyze complex dynamics from observed and simulated data. An advantage of extracting dynamic behaviors from data is that this approach enables the investigation of nonlinear phenomena whose mathematical models are unavailable. The purpose of this present work is to extract information about transition phenomena (e.g., mean exit time and escape probability), from data of stochastic differential equations with non-Gaussian Lévy noise. As a tool in describing dynamical systems, the Koopman semigroup transforms a nonlinear system into a linear system, but at the cost of elevating a finite dimensional problem into an infinite dimensional one. In spite of this, using the relation between the stochastic Koopman semigroup and the infinitesimal generator of a stochastic differential equation, we learn the mean exit time and escape probability from data. Specifically, we first obtain a finite dimensional approximation of the infinitesimal generator by an extended dynamic mode decomposition algorithm. Then we identify the drift coefficient, diffusion coefficient and anomalous diffusion coefficient for the stochastic differential equation. Finally, we compute the mean exit time and escape probability by finite difference discretization of the associated nonlocal partial differential equations. This approach is applicable in extracting transition information from data of stochastic differential equations with either (Gaussian) Brownian motion or (non-Gaussian) Lévy motion. We present one - and two-dimensional examples to demonstrate the effectiveness of our approach.

arXiv comment27 pages, 12 figures

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• 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
• Lévy-noise versus Gaussian-noise-induced Transitions in the Ghil-Sellers Energy Balance Model
• Learning the temporal evolution of multivariate densities via normalizing flows
• An end-to-end deep learning approach for extracting stochastic dynamical systems with $\alpha$-stable Lévy noise
• Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas
• Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time