{"id":9122,"date":"2022-11-16T15:34:41","date_gmt":"2022-11-16T14:34:41","guid":{"rendered":"https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/"},"modified":"2022-11-16T15:34:41","modified_gmt":"2022-11-16T14:34:41","slug":"machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/","title":{"rendered":"Machine Learning Seminar meeting: A time-evolving digital twin tool for engineering dynamics applications"},"content":{"rendered":"

Abstract:<\/strong><\/p>

In this presentation we describe a time-evolving digital twin and its application to a proof-of-concept engineering dynamics example. The digital twin is constructed by combining physics-based and data-based models of the physical twin, using a weighting technique. The resulting model combination enables the temporal evolution of the digital twin to be optimised based on the data recorded from the physical twin. This is achieved by creating digital twin output functions that are optimally-weighted combinations of physics- and\/or data-based model components that can be updated over time to reflect the behaviour of the physical twin as accurately as possible. Approximate Bayesian computation (ABC) is used for the physics-based model in this work, on the premise that relatively simple physical models are only rarely available in the context of digital twins. For the data-based model, a nonlinear auto-regressive exogeneous (NARX) neural network model was used. The engineering dynamics example is a system consisting of two cascading tanks driven by a pump. The data received by the digital twin is segmented so that the process can be carried out over relatively short time-scales. In this example, the weightings are computed based on error and robustness criteria. The results show how the time-varying water level in the tanks can be captured with the digital twin output functions, and a comparison is made with three different weighting choice criteria.<\/p>

Prof. David Wagg<\/a><\/strong>\u00a0is a Professor of Nonlinear Dynamics at the University of Sheffield<\/strong>. His research is focused on understanding and controlling nonlinear structural dynamics. Some of his current research activities relate to, so called, \u00ab\u00a0digital twins\u00a0\u00bb, where data and models are combined to create a \u00ab\u00a0virtual duplicate\u00a0\u00bb of the structure of interest.<\/p>

The Machine Learning Seminar<\/strong> is a regular weekly seminar series aiming to harbor presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.\u00a0More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/<\/a><\/p>

To register please send a mail to\u00a0Dr. Jakub Lengiewicz.<\/strong><\/p>

You can now watch a recording of the seminar here:<\/strong><\/p>

\n \n\"\"\n <\/figure><\/strong><\/a><\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"

Abstract:In this presentation we describe a time-evolving digital twin and its application to a proof-of-concept engineering dynamics example. The digital twin is constructed by combining physics-based and data-based models of the physical twin, using a weighting technique. The resulting model combination enables the temporal evolution of the digital twin to be optimised based on the data recorded from the physical twin. This is achieved by creating digital twin output functions that are optimally-weighted combinations of physics- and\/or data-based model components that can be updated over time to reflect the behaviour of the physical twin as accurately as possible. Approximate Bayesian computation (ABC) is used for the physics-based model in this work, on the premise that relatively simple physical models are only rarely available in the context of digital twins. For the data-based model, a nonlinear auto-regressive exogeneous (NARX) neural network model was used. The engineering dynamics example is a system consisting of two cascading tanks driven by a pump. The data received by the digital twin is segmented so that the process can be carried out over relatively short time-scales. In this example, the weightings are computed based on error and robustness criteria. The results show how the time-varying water level in the tanks can be captured with the digital twin output functions, and a comparison is made with three different weighting choice criteria.Prof. David Wagg\u00a0is a Professor of Nonlinear Dynamics at the University of Sheffield. His research is focused on understanding and controlling nonlinear structural dynamics. Some of his current research activities relate to, so called, \u00ab\u00a0digital twins\u00a0\u00bb, where data and models are combined to create a \u00ab\u00a0virtual duplicate\u00a0\u00bb of the structure of interest.The Machine Learning Seminar is a regular weekly seminar series aiming to harbor presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.\u00a0More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/To register please send a mail to\u00a0Dr. Jakub Lengiewicz.<\/p>\n","protected":false},"author":0,"featured_media":9123,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2022-11-23 10:00:00","event_end_date":"2022-11-23 11:00:00","event_speaker_name":"David Wagg, Department of Mechanical Engineering, University of Sheffield","event_speaker_link":"","event_is_online":false,"event_location":"Online","event_street":"","event_location_link":"","event_zip_code":"","event_city":"","event_country":"LU"},"events-topic":[308],"events-type":[],"organisation":[43,25,226],"authorship":[],"acf":[],"yoast_head":"\nMachine Learning Seminar meeting: A time-evolving digital twin tool for engineering dynamics applications - Universit\u00e9 du Luxembourg<\/title>\n<meta name=\"description\" content=\"Abstract:In this presentation we describe a time-evolving digital twin and its application to a proof-of-concept engineering dynamics example. 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The data received by the digital twin is segmented so that the process can be carried out over relatively short time-scales. In this example, the weightings are computed based on error and robustness criteria. The results show how the time-varying water level in the tanks can be captured with the digital twin output functions, and a comparison is made with three different weighting choice criteria.Prof. David Wagg\u00a0is a Professor of Nonlinear Dynamics at the University of Sheffield. His research is focused on understanding and controlling nonlinear structural dynamics. Some of his current research activities relate to, so called, \"digital twins\", where data and models are combined to create a \"virtual duplicate\" of the structure of interest.The Machine Learning Seminar is a regular weekly seminar series aiming to harbor presentations of fundamental and methodological advances in data science and machine learning as well as to discuss application areas presented by domain specialists. The uniqueness of the seminar series lies in its attempt to extract common denominators between domain areas and to challenge existing methodologies. The focus is thus on theory and applications to a wide range of domains, including Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.\u00a0More information about the ML Seminar, together with video recordings from past meetings you will find here: https:\/\/legato-team.eu\/seminars\/To register please send a mail to\u00a0Dr. Jakub Lengiewicz.","breadcrumb":{"@id":"https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/#breadcrumb"},"inLanguage":"fr-FR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/"]}]},{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/#primaryimage","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2022\/11\/machine_learning_seminar_meeting_a_time_evolving_digital_twin_tool_for_engineering_dynamics_applications.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2022\/11\/machine_learning_seminar_meeting_a_time_evolving_digital_twin_tool_for_engineering_dynamics_applications.jpg","width":800,"height":600},{"@type":"BreadcrumbList","@id":"https:\/\/www.uni.lu\/fr\/events\/machine-learning-seminar-meeting-a-time-evolving-digital-twin-tool-for-engineering-dynamics-applications\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.uni.lu\/fr\/"},{"@type":"ListItem","position":2,"name":"Events","item":"https:\/\/www.uni.lu\/fr\/events\/"},{"@type":"ListItem","position":3,"name":"Machine Learning Seminar meeting: A time-evolving digital twin tool for engineering dynamics applications"}]},{"@type":"WebSite","@id":"https:\/\/www.uni.lu\/fr\/#website","url":"https:\/\/www.uni.lu\/fr\/","name":"Uni.lu","description":"Universit\u00e9 du Luxembourg","publisher":{"@id":"https:\/\/www.uni.lu\/fr\/#organization"},"alternateName":"Universit\u00e9 du Luxembourg","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.uni.lu\/fr\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"fr-FR"},{"@type":"Organization","@id":"https:\/\/www.uni.lu\/fr\/#organization","name":"Universit\u00e9 du Luxembourg","alternateName":"Uni.lu","url":"https:\/\/www.uni.lu\/fr\/","logo":{"@type":"ImageObject","inLanguage":"fr-FR","@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/","url":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","contentUrl":"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/11\/2026\/03\/03120045\/UNIV_SM-Profile_1600x1600px-scaled.jpg","width":2560,"height":2560,"caption":"Universit\u00e9 du Luxembourg"},"image":{"@id":"https:\/\/www.uni.lu\/fr\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/uni.lu","https:\/\/www.linkedin.com\/school\/university-of-luxembourg\/","https:\/\/www.instagram.com\/uni.lu","https:\/\/www.youtube.com\/@uni_lu","https:\/\/en.wikipedia.org\/wiki\/University_of_Luxembourg"]}]}},"_links":{"self":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/9122"}],"collection":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events"}],"about":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/types\/events"}],"replies":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/comments?post=9122"}],"version-history":[{"count":0,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events\/9122\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media\/9123"}],"wp:attachment":[{"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/media?parent=9122"}],"wp:term":[{"taxonomy":"events-topic","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-topic?post=9122"},{"taxonomy":"events-type","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/events-type?post=9122"},{"taxonomy":"organisation","embeddable":true,"href":"https:\/\/www.uni.lu\/fr\/wp-json\/wp\/v2\/organisation?post=9122"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}