Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems

Understand

Advances in data science are leading to new progresses in the analysis and understanding of complex dynamics for systems with experimental and observational data.

• With numerous physical phenomena exhibiting bursting, flights, hopping, and intermittent features, stochastic differential equations with non-Gaussian L\'evy noise are suitable to model these systems.
• Thus it is desirable and essential to infer such equations from available data to reasonably predict dynamical behaviors.
• In this work, we consider a data-driven method to extract stochastic dynamical systems with non-Gaussian asymmetric (rather than the symmetric) L\'evy process, as well as Gaussian Brownian motion.