Strong Solutions for Stochastic Partial Differential Equations of Gradient Type

Details Strong Solutions for Stochastic Partial Differential Equations of Gradient Type Strong Solutions for Stochastic Partial Differential Equations of Gradient Type

2011-04-21

arxiv.org/abs/1104.4243v1

Mathematics

Probability

30 pages

Scientific paper

Unique existence of analytically strong solutions to stochastic partial differential equations (SPDE) with drift given by the subdifferential of a quasi-convex function and with general multiplicative noise is proven. The proof applies a genuinely new method of weighted Galerkin approximations based on the "distance" defined by the quasi-convex function. Spatial regularization of the initial condition analogous to the deterministic case is obtained. The results yield a unified framework which is applied to stochastic generalized porous media equations, stochastic generalized reaction diffusion equations and stochastic generalized degenerated p-Laplace equations. In particular, higher regularity for solutions of such SPDE is obtained.

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