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Averaging principle for slow–fast stochastic 2D Navier–Stokes equation driven by Lévy noise
Author(s) -
Gao Junrong,
Li Shihu,
Sun Xiaobin,
Xie Yingchao
Publication year - 2020
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.7123
Subject(s) - mathematics , multiplicative noise , discretization , noise (video) , scale (ratio) , mathematical analysis , stochastic differential equation , navier–stokes equations , brownian noise , white noise , physics , computer science , statistics , artificial intelligence , image (mathematics) , signal transfer function , digital signal processing , quantum mechanics , analog signal , compressibility , computer hardware , thermodynamics
This paper investigates the multiscale stochastic 2D Navier–Stokes equation driven by multiplicative Lévy noise. To be more precise, we establish the strong averaging principle for the stochastic 2D Navier–Stokes equation driven by Lévy noise, which involves a fast time scale component governed by a stochastic reaction‐diffusion equation driven by Lévy noise. The Khasminkii's time discretization approach and the technique of stopping time play the important roles in our proof.