Jiang-Wei Zhang

Physics-Informed Neural Networks for One-Dimensional Groundwater Contaminant Transport: A Synthetic Numerical Study of Prediction and Parameter Inversion

The results show that PINNs accurately predict concentration values and reduce initialization-induced uncertainty when the physical constraints—including the ADE residual and the prescribed initial and boundary conditions—cover the target prediction period, outperforming purely data-driven neural networks in both accur...