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.