Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks

The constitutive relations of the Richardson-Richards equation encode the macroscopic properties of soil water retention and conductivity. These soil hydraulic functions are commonly represented by models with a handful of parameters. The limited degrees of freedom of such soil hydraulic models constrain our ability to extract soil hydraulic properties from soil moisture data via inverse modeling. We present a new free-form approach to learning the constitutive relations using physically constrained neural networks. We implemented the inverse modeling framework in a differentiable modeling framework, JAX, to ensure scalability and extensibility. For efficient gradient computations, we implemented implicit differentiation through a nonlinear solver for the Richardson-Richards equation. We tested the framework against synthetic noisy data and demonstrated its robustness against varying magnitudes of noise and degrees of freedom of the neural networks. We applied the framework to soil moisture data from an upward infiltration experiment and demonstrated that the neural network-based approach was better fitted to the experimental data than a parametric model and that the framework can learn the constitutive relations. We developed a fully-differentiable solver for the Richardson-Richards equation The constitutive relations are represented by physically constrained neural networks The framework can be used to extract soil hydraulic properties without assuming coupling between the constitutive relations