In this present study, gene expression programing (GEP) has been used for training a model
for the subgrid scale (SGS) scalar dissipation rate (SDR) for a large range of filter widths …

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection
through the synergistic use of thermal and RGB imaging, coupled with advanced deep …

Data-driven methods for predicting aerodynamic coefficients of arbitrary shapes have
received considerable attention due to their flexibility and scalability. This paper introduces a …

This study explores the implementation of physics-informed neural networks (PINNs) to
analyze turbulent flow in composite porous-fluid systems. These systems are composed of a …

Deep learning (DL) based methods have recently made remarkable progress in remote
sensing (RS) soil moisture (SM) retrieval applications. However, their purely “black box” …

This research focuses on leveraging fusion imaging, which combines thermal and RGB
data, for the inspection of Wind Turbine Blades. We introduce a novel dataset comprising …

The manipulation of crystallographic defects in 2H-transition metal dichalcogenides (2H-
TMDCs), whether pre-or post-synthesis, has garnered significant interest recently, as it holds …

A filtered reaction rate model driven by deep learning is proposed and analyzed a priori in
the context of large eddy simulation (LES). A deep artificial neural network (ANN) is trained …

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused
by the imbalance between capacity and demand in an airspace network. Furthermore, it is …

Flood is one of the worst natural disasters, which causes significant damage to economy
and society. Flood risk assessment helps to reduce flood damage by managing flood risk in …