Inverse analysis of unsaturated soil hydraulic parameters using Physics-Informed Neural Networks

Abstract

Physics-informed neural networks (PINNs) have been proposed as a method for incorporating physical law into deep learning by introducing partial differential equations, boundary conditions, and initial conditions into the loss function. This study conducted inverse analysis of parameters related to unsaturated soil hydraulic properties by PINNs through the reproduction of test results using soil column tests. Inverse analysis of unsaturated soil hydraulic parameters based on soil column method data using PINNs revealed that it is possible to estimate the parameters that captured the characteristics of soil samples used in the tests. Therefore, PINNs is an available method for the inverse analysis of unsaturated seepage hydraulic parameters.