Environmental hazards such as floods significantly frustrate the functionality of built assets.
In addressing flood-induced challenges, data usage has become important. Despite existing …

Modeling complex physical processes such as the extraction of aluminum is mainly done
using pure physics-based models derived from first principles. However, the accuracy of …

Deep neural networks have become very popular in modeling complex nonlinear processes
due to their extraordinary ability to fit arbitrary nonlinear functions from data with minimal …

A digital twin is a powerful tool that can help monitor and optimize physical assets in real-
time. Simply put, it is a virtual representation of a physical asset, enabled through data and …

In the dynamic landscape of renewable energy, the primary goal continues to be the
enhancement of competitiveness through the implementation of cutting-edge technologies …

Digital twins bring the potential to increase the efficiency of assets, systems, and processes
by building virtual replicas through real-time data and modeling. However, data are often …

Digital coherent transceivers have developed to the stage that they can monitor the physical
state of an optical network and thus are capable of generating data to build measurement …

In the field of prognostics and health management for industrial equipment, digital twins
stand out as essential tools. Wind park operators can harness the potential of digital twins to …

Here, a comparative investigation of data-driven, physics-based, and hybrid models for the
fatigue lifetime prediction of structural adhesive joints in terms of complexity of …

Physics‐informed neural network (PINN) has been widely concerned for its higher
computational accuracy compared with conventional neural network. The merit of PINN …