Groundwater inverse modeling: Physics-informed neural network with disentangled constraints and errors

• Published in: Journal of hydrology (Amsterdam) Vol. 640; p. 131703
• Main Authors: Ji, Yuzhe, Zha, Yuanyuan, Yeh, Tian-Chyi J., Shi, Liangsheng, Wang, Yanling
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2024
• Subjects: Boundary conditions; Groundwater inversion; Hydraulic tomography; Physics-informed neural network; Physics-informed neural network; Boundary conditions; Hydraulic tomography; Groundwater inversion

•A PINN method (KLE-PINN) is proposed for estimating hydraulic conductivity under different scenarios.•Analyzing water head fitting error improve understanding the results of our model.•KLE-PINN can easily investigate cases where BCs are unknown. This study combines a physics-informed neural network...