A FUNDAMENTAL STUDY ON UNSATURATED SEEPAGE SIMULATION BY PHYSICS-INFORMED NEURAL NETWORKS

Abstract

Physics-informed neural networks (PINNs) have developed as a deep learning method that can output solutions based on physical laws. This study verified the applicability of PINNs to unsaturated seepage simulation through the reproduction of test results using soil column tests, and discussed the methods to construct an accurate PINNs model. It was clarified that the unsaturated seepage simulation using PINNs was sufficiently available. Furthermore, we could construct an accurate PINNs model for unsaturated seepage simulation by using the sum of squared error as the loss function and the pressure head as the physical quantity output from the PINNs model.