A numerical study into neural network surrogate model performance for uncertainty propagation

ArXi:2605.16078v1 Announce Type: cross Neural network surrogate models have emerged as a promising approach to model solution fields for a wide variety of boundary value problems encountered in physical modeling. Stochastic problems represent an area of particularly high interest because of the potential to significantly reduce the repeated evaluation of expensive forward models via traditional numerical solvers when conducting parametric analysis.