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Journal of Functional Analysis
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Journal of Functional Analysis
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Stochastic Volterra equations in Banach spaces and stochastic partial differential equation

Authors: Zhang, Xicheng;

Stochastic Volterra equations in Banach spaces and stochastic partial differential equation

Abstract

In this paper, we first study the existence-uniqueness and large deviation estimate of solutions for stochastic Volterra integral equations with singular kernels in 2-smooth Banach spaces. Then, we apply them to a large class of semilinear stochastic partial differential equations (SPDE) driven by Brownian motions as well as by fractional Brownian motions, and obtain the existence of unique maximal strong solutions (in the sense of SDE and PDE) under local Lipschitz conditions. Lastly, high order SPDEs in a bounded domain of Euclidean space, second order SPDEs on complete Riemannian manifolds, as well as stochastic Navier-Stokes equations are investigated.

65Pages

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Keywords

Stochastic Navier–Stokes equation, large deviation, Probability theory on linear topological spaces, Probability (math.PR), 60H15, 35R60, stochastic Navier-Stokes equation, Large deviation, Mathematics - Analysis of PDEs, Stochastic partial differential equations (aspects of stochastic analysis), FOS: Mathematics, stochastic Volterra equation, Stochastic Volterra equation, Analysis, Mathematics - Probability, Analysis of PDEs (math.AP)

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
83
Top 1%
Top 10%
Top 10%
Green
hybrid