Prior mapping for nonlinear flows in random environments

We analyze nonlinear flows in randomly heterogeneous environments, which are characterized by state-dependent diffusion coefficients with spatially correlated structures. The prior Kirchhoff mapping is used to describe such systems by linear stochastic partial differential equations with multiplicative noise. These are solved through moment equations which are closed, alternatively, either by perturbation expansions, or by a posterior linear mapping closure. The latter relies on the assumption that the state variable is a spatially distributed Gaussian field. We demonstrate that the former approach is more robust.