This study explores the applicability of physics-informed neural networks to synthetic and real-world groundwater case studies, encompassing heterogeneous and homogeneous aquifers with varying boundary conditions and transient states, and reveals that PINNs provide a compelling alternative to classic ML methods, particularly in handling complex heterogeneity of aquifer properties.
An attention-based pure deep learning model is proposed to predict weekly groundwater levels of 28 piezometers in the Cuneo and Torino provinces in Piedmont (Italy), leveraging both irregular groundwater time series and weather image sequences by considering physics-guided strategies to inject the groundwater flow equa...
Matteo Salis, Gabriele Sartor, Rosa Meo et al.· Machine Learning: Science an...· 0 citations
The results show that PINNs accurately predict concentration values and reduce initialization-induced uncertainty when the physical constraints—including the ADE residual and the prescribed initial and boundary conditions—cover the target prediction period, outperforming purely data-driven neural networks in both accur...
There is growing demand for river-network models that can estimate sediment transport at fine temporal resolution. Although coupling physically based sediment modules with hydrological and hydrodynamic models is a logical path forward, these approaches require intensive calibration and substantial computing power, li...
A. Haddadchi, Neshat Movahedi, Reza Akbarian-Bafghi et al.· Journal of Hydraulic Enginee...· 0 citations
An integrated set of methodologies that applies both three-dimensional computational fluid dynamics (CFD) and cutting-edge machine learning (ML) and deep learning (DL) methodologies for the environmental thermal assessment is provided.
U. Khusankhodzhaev, Laziz Qayimov, Shakhodat Kobilova et al.· EPJ Web of Conferences· 0 citations
Geological heterogeneity poses a major challenge to predicting subsurface fluid–contaminant migration and assessing groundwater vulnerability. This study develops a physics-informed predictive framework that integrates geophysical inversion with machine learning (ML) to improve the prediction and interpretation of cont...
D. Adepehin, F. F. Magi, Patrick Yagazie Anyanwu et al.· UMYU Scientifica· 0 citations
Methane hydrates hold enormous quantities of natural gas in a form that could meaningfully add to the world's future energy supply, yet accurately forecasting how productive a given reservoir will be remains difficult. The obstacle is coupling: thermal, hydraulic, mechanical, and geochemical processes all interact duri...
Saiful Alam· International Journal of Sci...· 0 citations
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