Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning
Data‐driven scientific discovery methods have been developed and applied to discover
governing equations from data, involving the attempt to discover the unsaturated flow …
governing equations from data, involving the attempt to discover the unsaturated flow …
Data‐driven discovery of soil moisture flow governing equation: A sparse regression framework
Conventionally, soil moisture dynamics are mathematically modeled by the Richardson‐
Richards equation, whose derivation is based on the conservation of mass and the …
Richards equation, whose derivation is based on the conservation of mass and the …
Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain …
T Bandai, TA Ghezzehei - Hydrology and Earth System …, 2022 - hess.copernicus.org
Modeling water flow in unsaturated soils is vital for describing various hydrological and
ecological phenomena. Soil water dynamics is described by well-established physical laws …
ecological phenomena. Soil water dynamics is described by well-established physical laws …
Encoder–Decoder Convolutional Neural Networks for Flow Modeling in Unsaturated Porous Media: Forward and Inverse Approaches
The computational cost of approximating the Richards equation for water flow in unsaturated
porous media is a major challenge, especially for tasks that require repetitive simulations …
porous media is a major challenge, especially for tasks that require repetitive simulations …
The Effect of Different Optimization Strategies to Physics-Constrained Deep Learning for Soil Moisture Estimation
Soil moisture is a key hydrological parameter that has significant importance to human
society and the environment. Accurate modeling and monitoring of soil moisture in crop …
society and the environment. Accurate modeling and monitoring of soil moisture in crop …
A deep learning approach based on physical constraints for predicting soil moisture in unsaturated zones
Y Wang, W Wang, Z Ma, M Zhao, W Li… - Water Resources …, 2023 - Wiley Online Library
Water transport in the unsaturated zone is an important part of the hydrological cycle and is
the link between the atmosphere‒soil‐groundwater for material and energy transport. The …
the link between the atmosphere‒soil‐groundwater for material and energy transport. The …
A transfer learning physics-informed deep learning framework for modeling multiple solute dynamics in unsaturated soils
Modeling subsurface flow and transport phenomena is essential for addressing a wide
range of challenges in engineering, hydrology, and ecology. The Richards equation is a …
range of challenges in engineering, hydrology, and ecology. The Richards equation is a …
Multiphysics‐Informed Neural Networks for Coupled Soil Hydrothermal Modeling
Soil water and heat transport are two physical processes that are described by the
Richardson–Richards equation and heat transport equation, respectively. Soil water and …
Richardson–Richards equation and heat transport equation, respectively. Soil water and …
A comprehensive study of deep learning for soil moisture prediction
Soil moisture plays a crucial role in the hydrological cycle, but accurately predicting soil
moisture presents challenges due to the nonlinearity of soil water transport and variability of …
moisture presents challenges due to the nonlinearity of soil water transport and variability of …
Physics-informed identification of PDEs with LASSO regression, examples of groundwater-related equations
In recent years, the application of machine learning methods in the derivation of physical
governing equations has gained significant attention. This has become increasingly relevant …
governing equations has gained significant attention. This has become increasingly relevant …