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Rough stochastic PDEs
Author(s) -
Hairer M.
Publication year - 2011
Publication title -
communications on pure and applied mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.12
H-Index - 115
eISSN - 1097-0312
pISSN - 0010-3640
DOI - 10.1002/cpa.20383
Subject(s) - mathematics , semigroup , nonlinear system , class (philosophy) , type (biology) , measure (data warehouse) , mathematical analysis , computer science , ecology , physics , quantum mechanics , database , artificial intelligence , biology
In this article, we show how the theory of rough paths can be used to provide a notion of solution to a class of nonlinear stochastic PDEs of Burgers type that exhibit too‐high spatial roughness for classical analytical methods to apply. In fact, the class of SPDEs that we consider is genuinely ill‐posed in the sense that different approximations to the nonlinearity may converge to different limits. Using rough path theory, a pathwise notion of solution to these SPDEs is formulated, and we show that this yields a well‐posed problem that is stable under a large class of perturbations, including the approximation of the rough‐driving noise by a mollified version and the addition of hyperviscosity. We also show that under certain structural assumptions on the coefficients, the SPDEs under consideration generate a reversible Markov semigroup with respect to a diffusion measure that can be given explicitly. © 2011 Wiley Periodicals, Inc.