Learning Constitutive Relations From Soil Moisture Data via Physically Constrained Neural Networks
Toshiyuki Bandai,
Teamrat A. Ghezzehei,
Peishi Jiang
et al.
Abstract: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 net… Show more
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