Towards the application of machine learning in digital twin technology: a multi-scale review
Luigi NeleDepartment of Chemical, Materials and Industrial Manufacturing Engineering, University of Naples Federico II, Naples, ItalyGiulio MatteraDepartment of Chemical, Materials and Industrial Manufacturing Engineering, University of Naples Federico II, Naples, ItalyEmily W. YapFaculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW, 2522, AustraliaMario VozzaDAIMON Lab, CNR-ISMN, Bologna, ItalySilvestro VespoliDepartment of Chemical, Materials and Industrial Manufacturing Engineering, University of Naples Federico II, Naples, Italy
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Abstract
This review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal role of machine learning in shaping the development and integration of digital twin technology across multiple disciplines. Emphasising key features like multidisciplinarity and multi-scale aspects, the paper explores how data-driven techniques are employed for modelling, visualisation, monitoring, and optimisation within the digital twin framework, pinpointing the benefits introduced in the current state-of-the-art applications, and elucidates persisting challenges across various research fields, including advanced materials, smart buildings, and manufacturing systems.
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