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The concept and advantages of physics-based Digital Twins are presented. A Digital Twin is a virtual representation of real-world entities and processes, synchronized at a specified frequency and fidelity. The approach based on the physics-based simulations which can predict the physical values enables to achieve the predictive maintenance and optimal operations on Digital Twin systems for industrial equipment. Key technologies for the physics-based twin models with machine learning technologies are discussed.
9:30am - 10:00am ID: 418 / Keynote & Tutorial (Room C): 2 Regular_Abstract (short paper) Submission Topics: Electromagnetic Functional Materials and Adaptive Systems (EFMAS) Keywords: Material Design, Electrical Resistance Tomography
FAST INVERSION METHODS FOR TOMOGRAPHY AND MATERIAL DESIGN
Antonello TAMBURRINO, Vincenzo MOTTOLA
Università di Cassino e del Lazio Meridionale, Italy
In this paper, we review some of the most significant contributions of the Authors in the field of real-time soft-field tomography and electromagnetic material design, together with a discussion on new research directions.
Specifically, we present (i) the imaging methods based on the Monotonicity Principle, combining excellent performances, real-time operations and capability to handle the presence of nonlinear materials, (ii) a new non-iterative inversion method for Electrical Resistance Tomography, the Kernel Method and (iii) a new material design paradigm in the framework of electromagnetic fields.
