Dataset for: Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements by Toshiyuki Bandai and Teamrat A. Ghezzehei

Simulation of soil moisture dynamics is important for various fields, such as agriculture, hydrological modeling, and natural disasters. Such simulations are conducted by solving a partial differential equation (PDE) called the Richardson-Richards equation (RRE). Because the RRE is a highly non-linear PDE, we need to solve it numerically. Various numerical methods have been used to solve the RRE, such as the finite difference, finite element, and finite element volume method. In order for those numerical methods to produce correct solutions, they require precise information on initial and boundary conditions, as well as hydraulic properties of soils. In "Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements by Toshiyuki Bandai and Teamrat A. Ghezzehei," we presented an alternative numerical method to infer hydraulic properties of soils from volumetric water content measurements withtout the initial and boudary conditions using physics-informed neural networks (PINNs). In this repository, we provide all the datasets that are needed to reproduce the analysis conducted in the paper.