Leseprobe
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Stochastic Parameterizing Manifolds and Non-Markovian Reduced Equations
Shouhong Wang, Honghu Liu, Mickaël D. Chekroun
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Springer International Publishing 
Naturwissenschaften, Medizin, Informatik, Technik / Analysis
Beschreibung
In this second volume, a general approach is developed to provide approximate parameterizations of the "small" scales by the "large" ones for a broad class of stochastic partial differential equations (SPDEs). This is accomplished via the concept of parameterizing manifolds (PMs), which are stochastic manifolds that improve, for a given realization of the noise, in mean square error the partial knowledge of the full SPDE solution when compared to its projection onto some resolved modes. Backward-forward systems are designed to give access to such PMs in practice. The key idea consists of representing the modes with high wave numbers as a pullback limit depending on the time-history of the modes with low wave numbers. Non-Markovian stochastic reduced systems are then derived based on such a PM approach. The reduced systems take the form of stochastic differential equations involving random coefficients that convey memory effects. The theory is illustrated on a stochastic Burgers-type equation.
Kundenbewertungen
Stochastic Parameterizing Manifolds,, partial differential equations, 37L65,37D10,37L25,35B42,37L10,60H15,35R60, Non-Markovian Reduced Equations, ordinary differential equations, Stochastic Burgers-Type Equation, Pullback Characterization, Weak Non-Resonnance Conditions