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Popp M.", "last" : "Schäfer"}, {"first" : "M.", "last" : "Schäfer"} ], "doi" : "https://doi.org/10.1016/j.est.2024.113348", "bibtexKey": "hauslein2024dynamic" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2d4deb8d1cf4daf1840c551efe600fa14/annettegugel", "tags" : [ "Applied","Capacitive","Frequency","Impact","Measurements","Modeling","Response","Transformers.","Transient","Voltage","for","of","on","the" ], "intraHash" : "d4deb8d1cf4daf1840c551efe600fa14", "interHash" : "a0a9aacabcf0a355a7b9e2728e8e36c0", "label" : "Impact of Applied Voltage on the Frequency Response Measurements for Transient Modeling of Capacitive Voltage Transformers.", "user" : "annettegugel", "description" : "", "date" : "2025-05-02 11:16:38", "changeDate" : "2025-05-02 11:16:38", "count" : 2, "pub-type": "conference", "year": "2024", "url": "", "author": [ "Felipe Louis Probst","M. V. F. da Luz","Stefan Tenbohlen" ], "authors": [ {"first" : "Felipe Louis", "last" : "Probst"}, {"first" : "M. V. F.", "last" : "da Luz"}, {"first" : "Stefan", "last" : "Tenbohlen"} ], "pages": "1-4", "eventtitle" : "2024 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)", "venue" : "Berlin, Germany", "eventdate" : "August 18-22", "doi" : "10.1109/ICHVE61955.2024.10676160", "bibtexKey": "probst2024impact" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f51f7f36f0337c98dcb01b837a8d4c9d/michaeljarwitz", "tags" : [ "Modeling","laser","myown","peer","welding" ], "intraHash" : "f51f7f36f0337c98dcb01b837a8d4c9d", "interHash" : "03d81cab9525981894415535861d70a6", "label" : "Application of a physics-informed hybrid model\r\nwith additional output constraints for the\r\nprediction of the threshold of deep-penetration\r\nlaser welding", "user" : "michaeljarwitz", "description" : "", "date" : "2025-02-03 17:43:53", "changeDate" : "2025-02-03 17:43:53", "count" : 2, "pub-type": "presentation", "year": "2024", "url": "", "author": [ "Michael Jarwitz","Andreas Michalowski" ], "authors": [ {"first" : "Michael", "last" : "Jarwitz"}, {"first" : "Andreas", "last" : "Michalowski"} ], "abstract": "The quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast\r\nand reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional\r\noutput constraints for the prediction of the threshold of deep-penetration laser welding is presented. A \u201Cresidual model\u201D approach is used,\r\nwhere a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments\r\nand a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result\r\nfrom applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a\r\nreduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a blackbox\r\nmachine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an\r\nincreased compliance with physical boundary conditions by applying the additional output constraints.", "eventtitle" : "43rd International Congress on Applications of Lasers & Electro-Optics (ICALEO 2024)", "venue" : "Hollywood, CA, USA", "eventdate" : "4-7 Novemeber 2024", "bibtexKey": "jarwitz2024application" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2f51f7f36f0337c98dcb01b837a8d4c9d/ifsw", "tags" : [ "myown","welding","laser","peer","Modeling" ], "intraHash" : "f51f7f36f0337c98dcb01b837a8d4c9d", "interHash" : "03d81cab9525981894415535861d70a6", "label" : "Application of a physics-informed hybrid model\r\nwith additional output constraints for the\r\nprediction of the threshold of deep-penetration\r\nlaser welding", "user" : "ifsw", "description" : "", "date" : "2025-02-03 17:43:53", "changeDate" : "2025-02-03 17:43:53", "count" : 2, "pub-type": "presentation", "year": "2024", "url": "", "author": [ "Michael Jarwitz","Andreas Michalowski" ], "authors": [ {"first" : "Michael", "last" : "Jarwitz"}, {"first" : "Andreas", "last" : "Michalowski"} ], "abstract": "The quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast\r\nand reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional\r\noutput constraints for the prediction of the threshold of deep-penetration laser welding is presented. A \u201Cresidual model\u201D approach is used,\r\nwhere a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments\r\nand a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result\r\nfrom applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a\r\nreduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a blackbox\r\nmachine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an\r\nincreased compliance with physical boundary conditions by applying the additional output constraints.", "eventtitle" : "43rd International Congress on Applications of Lasers & Electro-Optics (ICALEO 2024)", "venue" : "Hollywood, CA, USA", "eventdate" : "4-7 Novemeber 2024", "bibtexKey": "jarwitz2024application" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2faa082ad04b2c7327f3eaab09bea7208/michaeljarwitz", "tags" : [ "Modeling","laser","myown","peer","welding" ], "intraHash" : "faa082ad04b2c7327f3eaab09bea7208", "interHash" : "95a19c8b28834f8d37b2890cbe087920", "label" : "Application of a physics-informed hybrid model\r\nwith additional output constraints for the\r\nprediction of the threshold of deep-penetration\r\nlaser welding", "user" : "michaeljarwitz", "description" : "", "date" : "2025-02-03 17:36:58", "changeDate" : "2025-02-03 17:36:58", "count" : 3, "pub-type": "article", "journal": "Journal of Laser Applications", "year": "2025", "url": "https://pubs.aip.org/lia/jla/article/37/1/012031/3333477/Application-of-a-physics-informed-hybrid-model", "author": [ "Michael Jarwitz","Andreas Michalowski" ], "authors": [ {"first" : "Michael", "last" : "Jarwitz"}, {"first" : "Andreas", "last" : "Michalowski"} ], "volume": "37","number": "1","abstract": "The quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast\r\nand reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional\r\noutput constraints for the prediction of the threshold of deep-penetration laser welding is presented. A \u201Cresidual model\u201D approach is used,\r\nwhere a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments\r\nand a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result\r\nfrom applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a\r\nreduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a blackbox\r\nmachine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an\r\nincreased compliance with physical boundary conditions by applying the additional output constraints.", "doi" : "https://doi.org/10.2351/7.0001547", "bibtexKey": "jarwitz2025application" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2faa082ad04b2c7327f3eaab09bea7208/ifsw", "tags" : [ "myown","welding","laser","peer","Modeling" ], "intraHash" : "faa082ad04b2c7327f3eaab09bea7208", "interHash" : "95a19c8b28834f8d37b2890cbe087920", "label" : "Application of a physics-informed hybrid model\r\nwith additional output constraints for the\r\nprediction of the threshold of deep-penetration\r\nlaser welding", "user" : "ifsw", "description" : "", "date" : "2025-02-03 17:36:58", "changeDate" : "2025-02-03 17:36:58", "count" : 3, "pub-type": "article", "journal": "Journal of Laser Applications", "year": "2025", "url": "https://pubs.aip.org/lia/jla/article/37/1/012031/3333477/Application-of-a-physics-informed-hybrid-model", "author": [ "Michael Jarwitz","Andreas Michalowski" ], "authors": [ {"first" : "Michael", "last" : "Jarwitz"}, {"first" : "Andreas", "last" : "Michalowski"} ], "volume": "37","number": "1","abstract": "The quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast\r\nand reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional\r\noutput constraints for the prediction of the threshold of deep-penetration laser welding is presented. A \u201Cresidual model\u201D approach is used,\r\nwhere a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments\r\nand a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result\r\nfrom applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a\r\nreduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a blackbox\r\nmachine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an\r\nincreased compliance with physical boundary conditions by applying the additional output constraints.", "doi" : "https://doi.org/10.2351/7.0001547", "bibtexKey": "jarwitz2025application" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/287a90ff180fc75de2afa88882003235a/petraheim", "tags" : [ "2024","Behavioral","Cyber-Physical","Fabric","Fabrication","Form-Finding","Interfaces","Making","Structures","Tangible","architecture","eidner","itke","maierhofer","menges","modeling","schwinn","turean" ], "intraHash" : "87a90ff180fc75de2afa88882003235a", "interHash" : "d90b6d336747330ae3e01d6265026e41", "label" : "Equilibrium Morphologies- Interactive modeling for form-finding of fabric structures", "user" : "petraheim", "description" : "", "date" : "2024-11-14 10:11:54", "changeDate" : "2024-11-14 10:11:54", "count" : 8, "pub-type": "inproceedings", "booktitle": "Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024)","publisher":"eCAADe", "year": "2024", "url": "", "author": [ "Fabian Eidner","Alina Turean","Samuel Leder","Mathias Maierhofer","Tobias Schwinn","Achim Menges" ], "authors": [ {"first" : "Fabian", "last" : "Eidner"}, {"first" : "Alina", "last" : "Turean"}, {"first" : "Samuel", "last" : "Leder"}, {"first" : "Mathias", "last" : "Maierhofer"}, {"first" : "Tobias", "last" : "Schwinn"}, {"first" : "Achim", "last" : "Menges"} ], "volume": "1","pages": "351\u2013 360","abstract": "While primarily admired for their material efficiency and aesthetic potentials, form-active fabric structures also offer original solutions to architectural design, modulating spaces with pliable and soft surface qualities. Their application, however, is hindered by the need for a profound understanding of the relationship between morphology, structure, and materialization. To comprehend and resolve these interdependencies, architects and engineers employ the means of form-finding. Existing form-finding methods for fabric structures exist either in purely digital or purely physical mediums. This paper introduces a cyber-physical form-finding method that seeks the equilibrium state of highly articulated fabric structures through sensorial modeling and emergent behavior of interacting forces. By embracing the softness and inherent responsiveness of elastic fabrics, this research presents an interactive form-finding approach for form-active material system where real-time shape adjustments are performed through hands-on manipulation. The developed design interface enables designers and user to swiftly explore numerous fabric morphologies in their equilibrium, suggesting intuitive tangible means of design communication.", "venue" : "Nicosia, Cyprus", "isbn" : "9789491207372", "language" : "eng", "eventdate" : "11. -13.09.2024", "issn" : "2684-1843", "bibtexKey": "eidner2024equilibrium" } ] }