36 citations as recorded by crossref.

1. Physics informed neural networks for learning the horizon size in bond-based peridynamic models F. Difonzo et al. 10.1016/j.cma.2024.117727
2. Inverse analysis for estimating geotechnical parameters using physics-informed neural networks S. Ito et al. 10.1016/j.sandf.2024.101533
3. Soil Science-Informed Machine Learning B. Minasny et al. 10.1016/j.geoderma.2024.117094
4. Physics-informed neural networks for modeling two-phase steady state flow with capillary heterogeneity at varying flow conditions A. Chakraborty et al. 10.1016/j.advwatres.2024.104639
5. Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems A. Sarma et al. 10.1016/j.cma.2024.117135
6. Modeling Water Flow and Solute Transport in Unsaturated Soils Using Physics‐Informed Neural Networks Trained With Geoelectrical Data P. Haruzi & Z. Moreno 10.1029/2023WR034538
7. Simultaneous Estimation of Soil Hydraulic and Thermal Properties Based on Multiobjective Optimization Algorithms J. Zhang & N. Li 10.3390/app15010337
8. Bayesian back analysis of unsaturated hydraulic parameters for rainfall-induced slope failure: A review H. Yang & L. Zhang 10.1016/j.earscirev.2024.104714
9. A multifactorial study of mass movement in the hilly and gully Loess Plateau based on intensive field surveys and remote sensing techniques L. Yan et al. 10.1016/j.scitotenv.2024.171628
10. A mixed pressure-velocity formulation to model flow in heterogeneous porous media with physics-informed neural networks F. Lehmann et al. 10.1016/j.advwatres.2023.104564
11. A generalized physics-driven neural network for micromechanical and microstructural evolution of heterogeneous materials Z. Xiong & P. Zhao 10.1016/j.euromechsol.2024.105551
12. Time series predictions in unmonitored sites: a survey of machine learning techniques in water resources J. Willard et al. 10.1017/eds.2024.14
13. Groundwater inverse modeling: Physics-informed neural network with disentangled constraints and errors Y. Ji et al. 10.1016/j.jhydrol.2024.131703
14. Physics informed neural networks for an inverse problem in peridynamic models F. Difonzo et al. 10.1007/s00366-024-01957-5
15. A data-driven physics-informed deep learning approach for estimating sub-core permeability from coreflooding saturation measurements A. Chakraborty et al. 10.1016/j.advwatres.2025.104919
16. Application of physics-informed neural networks (PINNs) solution to coupled thermal and hydraulic processes in silty sands Y. Feng et al. 10.1186/s40703-025-00232-w
17. Machine Learning and Artificial Intelligence Applications in Soil Science B. Minasny & A. McBratney 10.1111/ejss.70093
18. Causal prior-embedded physics-informed neural networks and a case study on metformin transport in porous media Q. Kang et al. 10.1016/j.watres.2024.121985
19. Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models S. Rubo & J. Zinkernagel 10.1016/j.agwat.2025.109402
20. A transfer learning physics-informed deep learning framework for modeling multiple solute dynamics in unsaturated soils H. Kamil et al. 10.1016/j.cma.2024.117276
21. A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics C. Zhao et al. 10.1063/5.0226562
22. Interaction Modeling of Surface Water and Groundwater: An Evaluation of Current and Future Issues N. Khurshid et al. 10.1007/s41101-025-00360-5
23. Some Remarks About Forward and Inverse Modelling in Hydrology, Within a General Conceptual Framework M. Giudici 10.3390/hydrology11110189
24. PINN enhanced extended multiscale finite element method for fast mechanical analysis of heterogeneous materials Z. Wu et al. 10.1007/s00707-024-03984-1
25. Modeling fluid flow in heterogeneous porous media with physics-informed neural networks: Weighting strategies for the mixed pressure head-velocity formulation A. Alhubail et al. 10.1016/j.advwatres.2024.104797
26. Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning W. Song et al. 10.1029/2022WR034122
27. Numerical modeling of one-dimensional variably saturated flow in a homogeneous and layered soil–water system via mixed form Richards equation with Picard iterative scheme S. Shah et al. 10.1007/s40808-022-01588-z
28. A novel approach to estimate sand particle-size using convolutional neural network with acoustic sensing Y. Sung et al. 10.1016/j.catena.2023.107639
29. High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS) Y. Xia & N. Li 10.3390/app14114521
30. Solving fluid flow in discontinuous heterogeneous porous media and multi-layer strata with interpretable physics-encoded finite element network X. Wang et al. 10.1016/j.jrmge.2024.10.025
31. Ensemble learning of soil–water characteristic curve for unsaturated seepage using physics-informed neural networks H. Yang et al. 10.1016/j.sandf.2024.101556
32. Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media S. Roy et al. 10.1016/j.finel.2024.104305
33. Hybrid data-mechanism-driven model of the unsteady soil temperature field for long-buried crude oil pipelines with non-isothermal batch transportation W. Jiang et al. 10.1016/j.energy.2024.130354
34. Inverse Physics-Informed Neural Networks for transport models in porous materials M. Berardi et al. 10.1016/j.cma.2024.117628
35. Application of improved physics-informed neural networks for nonlinear consolidation problems with continuous drainage boundary conditions P. Lan et al. 10.1007/s11440-023-01899-0
36. Forward and inverse modeling of water flow in unsaturated soils with discontinuous hydraulic conductivities using physics-informed neural networks with domain decomposition T. Bandai & T. Ghezzehei 10.5194/hess-26-4469-2022