[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-128821-105":59,"doc-detail-128821-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","physics-informed-machine-learning-for-inverse-problems-in-condition-monitoring-a-88","PHYSICS-INFORMED MACHINE LEARNING FOR INVERSE PROBLEMS IN CONDITION MONITORING - A - 88","","Modern societies rely on efficient, highly available transport infrastructure, making continuous bridge monitoring essential even under limited financial and human resources. As structures age, manual structural monitoring and damage assessment become increasingly time-consuming. Interpretable, sensor-based digital methods are required to enable cost-effective continuous condition monitoring. The work formulates damage assessment—detection, localization, and quantification—as inverse system identification from mechanical models using measured quantities.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/physics-informed-machine-learning-for-inverse-problems-in-condition-monitoring-a-88/128821/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/physics-informed-machine-learning-for-inverse-problems-in-condition-monitoring-a-88/128821.png","ImageObject",300,407,{"name":92,"@type":93},"Maeve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-20","2026-08-06",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is bridge condition monitoring becoming more demanding?","Question",{"text":113,"@type":114},"Bridge monitoring is increasingly time-consuming as structures age and their condition deteriorates, while resources remain limited.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How is damage assessment formulated in the talk?",{"text":118,"@type":114},"Damage assessment is posed as an inverse problem of system identification using a mechanical model that links system state to measurable quantities such as displacements and accelerations.",{"name":120,"@type":111,"acceptedAnswer":121},"Which approaches and validations are discussed?",{"text":122,"@type":114},"The talk surveys physics-informed neural networks and FEM-based neural networks or sparse Bayesian learning, tests them on a two-span beam numerical benchmark, and plans extensions to real sensor data with comparison to baseline operational modal analysis models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},128821,1786003691,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":81,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":39},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","\\#88  \nPHYSICS-INFORMED MACHINE LEARNING FOR INVERSE PROBLEMS IN CONDITION MONITORING  \nMax Von Danwitz 1, Philip Franz 1, Philipp Knechtges2, Alexander Popp1,3  \n1. German Aerospace Center (DLR), Institute for the Protection of Terrestrial Infrastructures, Sankt Augustin, Germany  \n2. German Aerospace Center (DLR), Institute for Software Technology, Sankt Augustin, Germany  \n3. University of the Bundeswehr Munich, Institute for Mathematics and Computer-Based Simulations (IMCS), Neubiberg, Germany  \nType: Abstract  \nCategory: IS15-INVERSE PROBLEMS IN DIGITAL TWINS:OPTIMAL DESIGN OF EXPERIMENTS, UNCERTAINTY QUANTIFICATION A  \nKeywords:  \nAbstract Text: Modern societies heavily rely on an efficient and highly available transport infrastructure, to provide their citizens with goods and services. To keep the highway and railway network fit-for-service, monitoring, repair and replacement of bridges must be ensured even with limited financial and human resources. The structural monitoring of bridges in particular is a manual process that becomes significantly more time-consuming as the structures age and their condition deteriorates. To meet the growing demand for cost-effective monitoring and damage assessment solutions, interpretable digital methods for continuous, sensor-based condition monitoring of structures are essential.  \nStarting form a mechanical model of a structure that takes the system state into account when responding to loads in terms of measurable quantities, such as displacements or accelerations, the task of damage assessment, including detection, localization and quantification, is formulated as inverse problem of system identification. This talk presents an overview of scientific machine learning methods that can contribute to solving this inverse problem, including physics-informed neural networks and FEM-based neural networks or sparse Bayesian learning [1, 2] .  \nSelected methods are tested with a numerical benchmark problem of a two-span beam structure [3] . The benchmark problem specifically accounts for variable operational and environmental conditions, such as variations of the ambient temperature regularly faced when monitoring bridges. Moreover, an extension to real-world sensor data from a measurement campaign on a two-span bridge and a comparison with base-line operational modal analysis models is planned [4] .  \nReferences:  \n[1] von Danwitz, M., Kochmann, T. T., Sahin, T., Wimmer, J., Braml, T., & Popp, A. (2023) . Hybrid Digital Twins: A Proof of Concept for Reinforced Concrete Beams. PAMM, 22(1), Article e202200146 . [https://doi.org/10.1002/pamm.202200146](https://doi.org/10.1002/pamm.202200146)  \n[2] Griese, F., Hoppe, F., Rüttgers, A., & Knechtges, P. (2024, September 6) . FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems. ArXiv preprint. [https://doi.org/10.48550/arXiv.2409.04067](https://doi.org/10.48550/arXiv.2409.04067)  \n[3] Tatsis, K., & Chatzi, E. (2019) . A numerical benchmark for system identification under operational  \nand environmental variability. In S. D. Amador, R. Brincker, E. I. Katsanos, M. López Aenlle, & P. Fernández (Eds.), 8th IOMAC-International Operational Modal Analysis Conference, Proceedings (pp. 101–106). International Group of Operations Modal Analysis. [https://doi.org/10.3929/ethz-b-000385231](https://doi.org/10.3929/ethz-b-000385231)  \n[4] Jaelani, Y., Klemm, A., Wimmer, J., Seitz, F., Köhncke, M., Marsili, F., Mendler, A., von Danwitz, M., Henke, S., Gündel, M., Braml, T., Spannaus, M., Popp, A., & Keßler, S. (2023). Developing a benchmark study for bridge monitoring. Steel Construction, 16(4), 215–225.  \n[https://doi.org/10.1002/stco.202200037](https://doi.org/10.1002/stco.202200037)  \n\\#88  \nPHYSICS-INFORMED MACHINE LEARNING FOR INVERSE PROBLEMS IN CONDITION MONITORING  \nMax Von Danwitz 1, Philip Franz 1, Philipp Knechtges2, Alexander Popp1,3  \n1. German Aerospace Center (DLR), Institute for the Protection of Terres","cbCaiuEf5XETrtRc","https://ap.wps.com/l/cbCaiuEf5XETrtRc","pdf",124322,"English","# Abstract\n## Motivation: bridge monitoring under resource constraints\n## Inverse problems for damage assessment\n## Physics-informed learning methods overview\n## Numerical benchmark and planned real-data validation","[{\"question\":\"Why is bridge condition monitoring becoming more demanding?\",\"answer\":\"Bridge monitoring is increasingly time-consuming as structures age and their condition deteriorates, while resources remain limited.\"},{\"question\":\"How is damage assessment formulated in the talk?\",\"answer\":\"Damage assessment is posed as an inverse problem of system identification using a mechanical model that links system state to measurable quantities such as displacements and accelerations.\"},{\"question\":\"Which approaches and validations are discussed?\",\"answer\":\"The talk surveys physics-informed neural networks and FEM-based neural networks or sparse Bayesian learning, tests them on a two-span beam numerical benchmark, and plans extensions to real sensor data with comparison to baseline operational modal analysis models.\"}]","PHYSICS-INFORMED MACHINE LEARNING FOR INVERSE PROBLEMS IN CONDITION MONITORING - A - 88 | PDF"]