Model-space learning of stochastic seawater intrusion using conditional normalizing flows

This paper introduces a conditional normalizing flow framework for learning the stochastic behaviour of seawater intrusion in coastal aquifers directly in model space. The method captures complex, non-Gaussian uncertainty and efficiently generates probabilistic intrusion predictions, supporting uncertainty-aware risk assessment and groundwater management decisions.