Submission Deadline: 30 April 2025 View: 198 Submit to Special Issue
Prof. Haojie Lian, Taiyuan University of Technology, China
Dr. Linwei He, University of Sheffield, UK
Prof. Peng Yu, Guangxi University, China
Dr. Elena Atroshchenko, University of New South Wales, Australia
Digital Twin and Virtual Reality (VR) are leading innovation in the era of Industrial 4.0. A digital twin is a virtual replica of a physical object, system, or process throughout its lifecycle. VR is a technology that gives users an immersive feel of a virtual world, allowing them to explore or interact with the simulated 3D environment. The VR can be used within digital twins by helping technicians visualize and understand the data collected from digital twins. The integration of digital twins and VR builds an online platform for digesting large quantities of real-time data and offering actionable insights within the product or process.
Two pillars underpinning digital twins and VR are physical simulation and machine learning. Compared to Computer-Aided Engineering (CAE), in which physical simulation was expensively used for validation of industrial product design, digital twin and VR require an interactive and real-time simulator exhibiting high performance in versatility, robustness, scalability, and fidelity. On the other hand, because data acquisition is often prohibitive in engineering, machine learning alone is not able to produce accurate prediction. Therefore, physical simulation and machine learning can complement each other, and their combination holds promises in driving the advancement of Digital Twin and Virtual reality.
The aim of this Special Issue is to bring together original research articles and review articles highlighting recent advances in integration of physical simulation and machine learning in digital twin and VR technology. Potential topics include but are not limited to the following:
• Real-time algorithm for interactive physical simulation.
• New 3D geometric modeling techniques.
• Data generation/augmentation via physical simulation for machine learning tasks.
• Haptic technology based on physical simulation.
• Physics-informed neural networks.
• Isogeometric analysis and other meshing burden alleviation techniques.
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