Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …

Construction of large underground infrastructure facilities routinely leads to leakage of
groundwater and reduction of pore water pressures, causing time-dependent deformation of …

Effective river management relies heavily on the accurate simulation of river flow dynamics
to develop scenario-based strategies and inform decision making. Deep learning (DL) …

Deep learning has gained attention as a potentially powerful technique for modeling natural-
state geothermal systems; however, its physical validity and prediction inaccuracy at …

Accurate real-time forecasts of atmospheric plume behavior are crucial for effective
management of environmental release incidents. However, the computational demands of …

Seawater intrusion into coastal aquifers poses a significant threat to groundwater resources,
especially with rising sea levels due to climate change. Accurate modeling and uncertainty …

Groundwater contaminant transport problems remain challenging with respect to their
computing requirements. Thus, it often limits the exploration of conceptual uncertainty, that is …