  A Physics-Informed Neural Network (PINN) provides a distinct advantage by
synergizing neural networks' capabilities with the problem's governing physical
laws. In this study, we introduce an innovative approach for solving seepage
problems by utilizing the PINN, harnessing the capabilities of Deep Neural
Networks (DNNs) to approximate hydraulic head distributions in seepage
analysis. To effectively train the PINN model, we introduce a comprehensive
loss function comprising three components: one for evaluating differential
operators, another for assessing boundary conditions, and a third for
appraising initial conditions. The validation of the PINN involves solving four
benchmark seepage problems. The results unequivocally demonstrate the
exceptional accuracy of the PINN in solving seepage problems, surpassing the
accuracy of FEM in addressing both steady-state and free-surface seepage
problems. Hence, the presented approach highlights the robustness of the PINN
and underscores its precision in effectively addressing a spectrum of seepage
challenges. This amalgamation enables the derivation of accurate solutions,
overcoming limitations inherent in conventional methods such as mesh generation
and adaptability to complex geometries.
