Groundwater modelling based on physics-informed neural networks

Groundwater is the world's largest accessible source of fresh water and plays an indispensable role in the global water cycle. Groundwater supports irrigation, supplies drinking water, and sustains baseflows to the surface expression of groundwater (e.g. rivers, ponds, wetlands). Simulations using physical numerical models are computationally expensive due to the heterogeneity of the actual groundwater flow and the complex initial and boundary conditions. Several surrogate models for reducing the computational burden have been proposed, however, they usually do not follow physics law. In this study, we intend to combine machine learning methods to analyse the feasibility of physics-informed neural networks (PINN) in groundwater modelling and propose a PINN groundwater model for the simulation of groundwater flow to improve computational efficiency while restricted to the physics law.