Dear Colleagues

Large equipment has the characteristics of high technological content and complex overall systems, which have a profound impact on modern industrial production. Equipment modeling is the most economical means to explore the status of equipment, which is particularly important for early troubleshooting and predictive maintenance of equipment. The state of any electronic device is closely related to the distribution of multi-physical fields in time and space. For example, digital twin technology fully utilizes data such as physical models, sensor updates, and operational history to integrate multi-disciplinary, multi-physical, multi-scale, and multi-probability simulation processes, completing mapping in virtual space, thereby reflecting the entire lifecycle process of corresponding physical equipment. Models based on physical mechanisms and big data are expected to drive the fourth industrial revolution, which will maximize the liberation of productivity.

This section will focus on the current state-of-the-art modeling methods for complex devices such as data-driven modeling methods, physical information driven methods, and modeling methods driven by both mechanism and data, their recent technological improvements in new devices, and emerging applications. Both original research papers and review articles describing the current state-of-the-art in this research field are welcome.

We look forward to receiving your contributions.

Dr. Xiao Qi

Section Editor

Optics, Electromagnetics; Acoustics; Thermodynamics; Machine learning; Sensing applications; Nondestructive testing method; Image processing techniques; Sensors design; Communication network; Energy management; Path Planning.