Fabio Musco

Deep learning methods for stochastic Galerkin approximations of random domain problems

A physics-informed neural network approach, based on the strong stochastic Galerkin residual, is compared with a Deep Ritz approach, based on the weak stochastic Galerkin system, reformulated as Ritz energy minimization, for solving high-dimensional coupled stochastic Galerkin systems.