[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125045-en":3,"doc-seo-125045-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125045,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Towards the application of machine learning in digital twin technology: a multi-scale review","This review article examines the conceptual foundation of digital twins and their applications across research domains, emphasizing the role of machine learning in enabling development and integration. It highlights multidisciplinarity and multi-scale perspectives, showing how data-driven methods support modelling, visualisation, monitoring, and optimisation within digital twin frameworks. The work summarises benefits reported in current state-of-the-art applications and clarifies remaining challenges across fields such as advanced materials, smart buildings, and manufacturing systems.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nTowards the application of machine learning in digital twin technology: a multi-scale review  \nOriginal  \nTowards the application of machine learning in digital twin technology: a multi-scale review / Nele, Luigi; Mattera, Giulio; Yap, Emily W. ; Vozza, Mario; Vespoli, Silvestro. -In: DISCOVER APPLIED SCIENCES. -ISSN 3004-9261. -6:10(2024) .[10.1007/s42452-024-06206-4]  \nAvailability:  \nThis version is available at: 11583/2992676 since: 2024-09-23T14:26:19Z  \nPublisher:  \nSpringer Nature  \nPublished  \nDOI:10.1007/s42452-024-06206-4  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n07 November 2024  \nReview  \nTowards the application of machine learning in digital twin technology: a multi‑scale review  \nLuigi Nele1 · Giulio Mattera1 · Emily W. Yap2 · Mario Vozza3,4 · Silvestro Vespoli1  \nReceived: 15 July 2024 / Accepted: 10 September 2024  \n© The Author(s) 2024 OPEN  \nAbstract  \nThis 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.  \nKeywords Digital twin · Advanced statistics · Machine learning · Materials · Smart buildings · Manufacturing  \n1 Introduction  \nNowadays, advancements in various technologies field such as the Internet of Things (IoT) and Artificial Intelligence (AI) facilitated the digitalisation of assets across diverse industrial sectors. In particular, Digital Twins (DTs) represent a disruptive technology that can be synthesised as an integrated multi-physics, multi-scale, probabilistic simulations of a physical asset that leverages complex physical models, sensor data and historical information is able to replicate the behaviour of their real-world counterparts [1, 2] .  \nDTs, integrated within Cyber Physical Systems (CPS) [3, 4], can be used for different goals like feedback control, asset optimisation, visualisation and support to decision-making [5, 6] .  \nThe concept of a DT surpasses that of a digital model, as demonstrated in Fig. 1. A digital model enables a unidirectional flow of data, originating from the physical object and feeding into its digital representation. The digital model adjusts itself based on input from the physical asset, without directly intervening in the physical entity. Additionally, a digital model can be utilised offline to simulate what-if scenarios, facilitating the optimisation of the physical asset’s performance. Conversely, Digital Twins possess bidirectional communication capabilities [7, 8] allowing them to communicate with the physical entity via automatic decisions—based on real-time events happening in the physical world—or via visualisation with human users. A typical example of a DT application is the predictive maintenance in manufacturing systems. In this case, the DT collect data from the physical entity and elaborate it in the digital world. If an anomalous  \n* Luigi Nele, [nele@unina.it |](nele@unina.it |1Department of Chemical)[1](nele@unina.it |1Department of Chemical)[Department of Chemical](nele@unina.it |1Department of Chemical), Materials and Industrial Manufacturing Engineering, University of Naples Federico II, Naples, Italy. 2Faculty of Engineering and Information Sciences, Universit","cbCaiu4mqMuKW1q3","https://ap.wps.com/l/cbCaiu4mqMuKW1q3","pdf",1789463,1,24,"English","en",105,"# Introduction\n## Digital twins within IoT and AI-driven digitalisation\n## Digital model vs digital twin: data flow and communication\n# Core components and reference framework\n## Perception layer and pre-processing\n## Digital object layer and state estimation\n## Application analysis layer and decision-making\n# Applications and challenges","[{\"question\":\"What distinguishes a digital model from a digital twin?\",\"answer\":\"A digital model follows a mainly unidirectional data flow from the physical asset to its digital representation, while a digital twin enables bidirectional communication with the physical entity through automatic decisions and user-oriented visualisation.\"},{\"question\":\"How does machine learning contribute to digital twin technology?\",\"answer\":\"Machine learning supports data-driven modelling, visualisation, monitoring, and optimisation, helping digital twins interpret data, forecast system states, and assist decision-making across multiple disciplines.\"},{\"question\":\"Which research fields are discussed as relevant to current digital twin applications?\",\"answer\":\"The review highlights applications and benefits in areas including advanced materials, smart buildings, and manufacturing systems, and it outlines persisting challenges across these fields.\"}]","Towards the application of machine learning in digital twin technology: a multi-scale review | 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distinguishes a digital model from a digital twin?","Question",{"text":75,"@type":76},"A digital model follows a mainly unidirectional data flow from the physical asset to its digital representation, while a digital twin enables bidirectional communication with the physical entity through automatic decisions and user-oriented visualisation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does machine learning contribute to digital twin technology?",{"text":80,"@type":76},"Machine learning supports data-driven modelling, visualisation, monitoring, and optimisation, helping digital twins interpret data, forecast system states, and assist decision-making across multiple disciplines.",{"name":82,"@type":73,"acceptedAnswer":83},"Which research fields are discussed as relevant to current digital twin applications?",{"text":84,"@type":76},"The review highlights applications and benefits in areas including advanced materials, smart buildings, and manufacturing systems, and it 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