Quantifying model uncertainty in dynamical systems driven by non-Gaussian Lévy stable noise with observations on mean exit time or escape probability

DOI10.1016/j.cnsns.2016.02.019OpenAlexW2289288363MaRDI QIDQ2200203

Ting Gao, Jin-qiao Duan

Publication date: 19 September 2020

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1306.0055

zbMATH Keywords

optimizationparameter estimationstochastic dynamical systemsmean exit timenon-Gaussian Lévy motiondouble-well system

Mathematics Subject Classification ID

Dynamical systems and ergodic theory (37-XX) Partial differential equations (35-XX)

Related Items (8)

Block splitting preconditioner for time-space fractional diffusion equations ⋮ Quantifying model uncertainty for the observed non-Gaussian data by the Hellinger distance ⋮ Detecting stochastic governing laws with observation on stationary distributions ⋮ Stochastic dynamics and data science ⋮ Identifying stochastic governing equations from data of the most probable transition trajectories ⋮ Extracting non-Gaussian governing laws from data on mean exit time ⋮ Parameter estimation via homogenization for stochastic dynamical systems with oscillating coefficients ⋮ Discovering mean residence time and escape probability from data of stochastic dynamical systems

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