Extracting non-Gaussian governing laws from data on mean exit time

DOI10.1063/5.0018812zbMath1456.37093arXiv2006.14974OpenAlexW3094679216WikidataQ103824628 ScholiaQ103824628MaRDI QIDQ5140882

Yanxia Zhang, Yanfei Jin, Yang Li, Jin-qiao Duan

Publication date: 17 December 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.14974

zbMATH Keywords

time seriesnon-Gaussian Lévy motionGaussian Brownian motionsparse regression technique

Mathematics Subject Classification ID

Processes with independent increments; Lévy processes (60G51) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Brownian motion (60J65) Time series analysis of dynamical systems (37M10) Nonlinear processes (e.g., (G)-Brownian motion, (G)-Lévy processes) (60G65)

Related Items (4)

A machine learning method for computing quasi-potential of stochastic dynamical systems ⋮ Stochastic dynamics and data science ⋮ Identifying stochastic governing equations from data of the most probable transition trajectories ⋮ Extracting governing laws from sample path data of non-Gaussian stochastic dynamical systems

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