Well-balanced stochastic Galerkin shallow flow model with uncertain topography

Stochastic Galerkin methods can quantify uncertainty at a fraction of the computational cost of simple Monte Carlo methods, but these methods have rarely been applied to shallow water models with uncertain terrain and uncertain friction source terms.

In this talk, I outline the pseudo-intrusive approach to formulate a stochastic Galerkin shallow water model, and discuss the impact on robust properties, accuracy and computational savings over simple Monte Carlo.