A comparative study of physics-informed neural network strategies for modeling water and nitrogen transport in unsaturated soils-Reference-Cited by-同舟云学术

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A comparative study of physics-informed neural network strategies for modeling water and nitrogen transport in unsaturated soils

Published:2025-11 Issue: Volume:661 Page:133624
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Kamil Hamza^ORCID,Soulaïmani Azzeddine^ORCID,Beljadid Abdelaziz^ORCID

Publisher

Elsevier BV

Reference65 articles.

1. TensorFlow: Large-scale machine learning on heterogeneous distributed systems;Abadi,2016

2. Residual-based attention in physics-informed neural networks;Anagnostopoulos;Comput. Methods Appl. Mech. Engrg.,2024

3. Modeling variably saturated multispecies reactive groundwater solute transport with MODFLOW-UZF and RT3D;Bailey;Groundwater,2013

4. 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;Bandai;Water Resour. Res.,2021

5. Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition;Bandai;Hydrol. Earth Syst. Sci.,2022

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