Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …

Deep learning (DL), a new generation of artificial neural network research, has transformed
industries, daily lives, and various scientific disciplines in recent years. DL represents …

In recent years significant breakthroughs in exploring big data, recognition of complex
patterns, and predicting intricate variables have been made. One efficient way of analyzing …

The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …

Surrogate strategies are used widely for uncertainty quantification of groundwater models in
order to improve computational efficiency. However, their application to dynamic multiphase …

Probabilistic inversion within a multiple‐point statistics framework is often computationally
prohibitive for high‐dimensional problems. To partly address this, we introduce and evaluate …

Segmentation is a critical step in Digital Rock Physics (DRP) as the original images are
available in a gray-scale format. Conventional methods often use thresholding to delineate …

The hydrologic community has experienced a surge in interest in machine learning in recent
years. This interest is primarily driven by rapidly growing hydrologic data repositories, as …

Identification of a groundwater contaminant source simultaneously with the hydraulic
conductivity in highly heterogeneous media often results in a high‐dimensional inverse …