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Ergodicity for a class of semilinear stochastic partial differential equations
Author(s) -
Dong Zhao,
Zhang Rangrang
Publication year - 2019
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.5938
Subject(s) - mathematics , stochastic partial differential equation , uniqueness , ergodicity , bounded function , white noise , stochastic differential equation , partial differential equation , mathematical analysis , multiplicative noise , invariant (physics) , burgers' equation , domain (mathematical analysis) , ergodic theory , parabolic partial differential equation , class (philosophy) , multiplicative function , mathematical physics , statistics , signal transfer function , digital signal processing , artificial intelligence , computer science , analog signal , electrical engineering , engineering
In this paper, we establish the existence and uniqueness of invariant measures for a class of semilinear stochastic partial differential equations driven by multiplicative noise on a bounded domain. The main results can be applied to SPDEs of various types such as the stochastic Burgers equation and the reaction‐diffusion equations perturbed by space‐time white noise.