BoomBoom
11-13-2017, 01:12 PM
http://www.hostpic.org/images/1711110935040076.jpg
Stochastic Parameterizing Manifolds and Non-Markovian Reduced Equations: Stochastic Manifolds for Nonlinear SPDEs II By MickaГ«l D. Chekroun, Honghu Liu, Shouhong Wang
2015 | 148 Pages | ISBN: 3319125192 | PDF | 4 MB
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.
DOWNLOAD
http://nitroflare.com/view/264F8AFA0E016F1/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar
http://rapidgator.net/file/3fee3582f75c911079cdff4922033298/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar.html
http://turbobit.net/96xhxh98720v/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar.html
Stochastic Parameterizing Manifolds and Non-Markovian Reduced Equations: Stochastic Manifolds for Nonlinear SPDEs II By MickaГ«l D. Chekroun, Honghu Liu, Shouhong Wang
2015 | 148 Pages | ISBN: 3319125192 | PDF | 4 MB
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.
DOWNLOAD
http://nitroflare.com/view/264F8AFA0E016F1/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar
http://rapidgator.net/file/3fee3582f75c911079cdff4922033298/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar.html
http://turbobit.net/96xhxh98720v/agn2t.S.P.M.a.NM.R.E.S.M.f.N.SPDE.II.R.rar.html