#7582. Application of physics-informed neural networks to inverse problems in unsaturated groundwater flow

October 2026publication date
Proposal available till 20-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Building and Construction;
Safety, Risk, Reliability and Quality;
Geology;
Civil and Structural Engineering;
Geotechnical Engineering and Engineering Geology;
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Abstract:
This paper investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in unsaturated groundwater flow. PINNs are applied to the types of unsaturated groundwater flow problems modelled with the Richards partial differential equation and the van Genuchten constitutive model. The inverse problem is formulated here as a problem with known or measured values of the solution to the Richards equation at several spatio-temporal instances, and unknown values of solution at the rest of the problem domain and unknown parameters of the van Genuchten model.
Keywords:
groundwater; infiltration; inverse; network; neural; Physics-informed; Richards; unsaturated

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