I. Zawra, C. Umunnakwe, M. Soestbergen, E. Rudnyi, T. Bechtold
{"title":"Stress Recovery in the Reduced Space for Parametric Reduced Models in Microelectronics","authors":"I. Zawra, C. Umunnakwe, M. Soestbergen, E. Rudnyi, T. Bechtold","doi":"10.1109/EuroSimE56861.2023.10100787","DOIUrl":null,"url":null,"abstract":"In the framework of the COMPAS project that is funded by EU [1], the goal to be achieved is structural health monitoring through standardizing compact digital twin generation. Model order reduction (MOR) is at the heart of generating compact digital twins that can be run in real-time or used in more statistical and optimization studies. Although the conventional stable MOR methods for linear systems reached a mature peak [2], the community still struggles to implement them efficiently for real-size industrial models. More advanced methods, like MOR for coupled-domain multi-physical systems or parametric model order reduction (pMOR) are still subject to research [3], [4].","PeriodicalId":425592,"journal":{"name":"2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 24th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EuroSimE56861.2023.10100787","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In the framework of the COMPAS project that is funded by EU [1], the goal to be achieved is structural health monitoring through standardizing compact digital twin generation. Model order reduction (MOR) is at the heart of generating compact digital twins that can be run in real-time or used in more statistical and optimization studies. Although the conventional stable MOR methods for linear systems reached a mature peak [2], the community still struggles to implement them efficiently for real-size industrial models. More advanced methods, like MOR for coupled-domain multi-physical systems or parametric model order reduction (pMOR) are still subject to research [3], [4].