Stochastic 2D Hydrodynamical Type Systems: Well Posedness and Large Deviations View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-06

AUTHORS

Igor Chueshov, Annie Millet

ABSTRACT

We deal with a class of abstract nonlinear stochastic models, which covers many 2D hydrodynamical models including 2D Navier-Stokes equations, 2D MHD models and the 2D magnetic Bénard problem and also some shell models of turbulence. We state the existence and uniqueness theorem for the class considered. Our main result is a Wentzell-Freidlin type large deviation principle for small multiplicative noise which we prove by a weak convergence method. More... »

PAGES

379-420

References to SciGraph publications

  • 1934-12. Sur le mouvement d'un liquide visqueux emplissant l'espace in ACTA MATHEMATICA
  • 1990-09. A new approach to energy theory in the stability of fluid motion in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1995-09. Martingale and stationary solutions for stochastic Navier-Stokes equations in PROBABILITY THEORY AND RELATED FIELDS
  • 2002-10. Stochastic 2-D Navier—Stokes Equation in APPLIED MATHEMATICS & OPTIMIZATION
  • 1972-01. Inéquations en thermoélasticité et magnétohydrodynamique in ARCHIVE FOR RATIONAL MECHANICS AND ANALYSIS
  • 1997-01. The Bénard problem with random perturbations: dissipativity and invariant measures in NONLINEAR DIFFERENTIAL EQUATIONS AND APPLICATIONS NODEA
  • 2010-02. Large Deviations for Stochastic Evolution Equations with Small Multiplicative Noise in APPLIED MATHEMATICS & OPTIMIZATION
  • 1990. Magnetohydrodynamics in NONE
  • 2007-09. Existence and Ergodicity for the Two-Dimensional Stochastic Magneto-Hydrodynamics Equations in APPLIED MATHEMATICS & OPTIMIZATION
  • 2006-11. Some Rigorous Results on a Stochastic GOY Model in JOURNAL OF STATISTICAL PHYSICS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00245-009-9091-z

    DOI

    http://dx.doi.org/10.1007/s00245-009-9091-z

    DIMENSIONS

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