{ "types" : { "Bookmark" : { "pluralLabel" : "Bookmarks" }, "Publication" : { "pluralLabel" : "Publications" }, "GoldStandardPublication" : { "pluralLabel" : "GoldStandardPublications" }, "GoldStandardBookmark" : { "pluralLabel" : "GoldStandardBookmarks" }, "Tag" : { "pluralLabel" : "Tags" }, "User" : { "pluralLabel" : "Users" }, "Group" : { "pluralLabel" : "Groups" }, "Sphere" : { "pluralLabel" : "Spheres" } }, "properties" : { "count" : { "valueType" : "number" }, "date" : { "valueType" : "date" }, "changeDate" : { "valueType" : "date" }, "url" : { "valueType" : "url" }, "id" : { "valueType" : "url" }, "tags" : { "valueType" : "item" }, "user" : { "valueType" : "item" } }, "items" : [ { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/291f54fe318fd82138164bcd9e2763d27/mathematik", "tags" : [ "anm","blood","dimensionally","flow","from:britsteiner","ians","kernel","methods,","mixedâ\u0080\u0090dimension","models,","peripheral","realâ\u0080\u0090time","reduced","simulations","simulations,","stenosis,","surrogate" ], "intraHash" : "91f54fe318fd82138164bcd9e2763d27", "interHash" : "979f2097ba9c22d67096e59e5c6d7a3e", "label" : "Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods", "user" : "mathematik", "description" : "", "date" : "2021-09-29 14:35:10", "changeDate" : "2021-10-15 08:38:35", "count" : 4, "pub-type": "article", "journal": "International Journal for Numerical Methods in Biomedical Engineering", "year": "2018", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.3095", "author": [ "Tobias Köppl","Gabriele Santin","Bernard Haasdonk","Rainer Helmig" ], "authors": [ {"first" : "Tobias", "last" : "Köppl"}, {"first" : "Gabriele", "last" : "Santin"}, {"first" : "Bernard", "last" : "Haasdonk"}, {"first" : "Rainer", "last" : "Helmig"} ], "volume": "34","number": "8","pages": "e3095","note": "e3095 cnm.3095","abstract": "Summary In this work, we consider two kinds of model reduction techniques\n\tto simulate blood flow through the largest systemic arteries, where\n\ta stenosis is located in a peripheral artery i.e. in an artery that\n\tis located far away from the heart. For our simulations we place\n\tthe stenosis in one of the tibial arteries belonging to the right\n\tlower leg (right post tibial artery). The model reduction techniques\n\tthat are used are on the one hand dimensionally reduced models (1â\u0080\u0090D\n\tand 0â\u0080\u0090D models, the soâ\u0080\u0090called mixedâ\u0080\u0090dimension model) and on\n\tthe other hand surrogate models produced by kernel methods. Both\n\tmethods are combined in such a way that the mixedâ\u0080\u0090dimension models\n\tyield training data for the surrogate model, where the surrogate\n\tmodel is parametrised by the degree of narrowing of the peripheral\n\tstenosis. By means of a wellâ\u0080\u0090trained surrogate model, we show that\n\tsimulation data can be reproduced with a satisfactory accuracy and\n\tthat parameter optimisation or state estimation problems can be solved\n\tin a very efficient way. Furthermore it is demonstrated that a surrogate\n\tmodel enables us to present after a very short simulation time the\n\timpact of a varying degree of stenosis on blood flow, obtaining a\n\tspeedup of several orders over the full model.", "owner" : "santinge", "groups" : "haasdonk, santin", "eprint" : "https://onlinelibrary.wiley.com/doi/pdf/10.1002/cnm.3095", "file" : ":PDF/KSHH2017_www_preprint.pdf:PDF", "doi" : "10.1002/cnm.3095", "bibtexKey": "KSHH2018" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/291f54fe318fd82138164bcd9e2763d27/britsteiner", "tags" : [ "anm","blood","dimensionally","flow","ians","kernel","methods,","mixedâ\u0080\u0090dimension","models,","peripheral","realâ\u0080\u0090time","reduced","simulations","simulations,","stenosis,","surrogate" ], "intraHash" : "91f54fe318fd82138164bcd9e2763d27", "interHash" : "979f2097ba9c22d67096e59e5c6d7a3e", "label" : "Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods", "user" : "britsteiner", "description" : "", "date" : "2021-09-29 14:33:27", "changeDate" : "2021-09-29 12:35:04", "count" : 4, "pub-type": "article", "journal": "International Journal for Numerical Methods in Biomedical Engineering", "year": "2018", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.3095", "author": [ "Tobias Köppl","Gabriele Santin","Bernard Haasdonk","Rainer Helmig" ], "authors": [ {"first" : "Tobias", "last" : "Köppl"}, {"first" : "Gabriele", "last" : "Santin"}, {"first" : "Bernard", "last" : "Haasdonk"}, {"first" : "Rainer", "last" : "Helmig"} ], "volume": "34","number": "8","pages": "e3095","note": "e3095 cnm.3095","abstract": "Summary In this work, we consider two kinds of model reduction techniques\n\tto simulate blood flow through the largest systemic arteries, where\n\ta stenosis is located in a peripheral artery i.e. in an artery that\n\tis located far away from the heart. For our simulations we place\n\tthe stenosis in one of the tibial arteries belonging to the right\n\tlower leg (right post tibial artery). The model reduction techniques\n\tthat are used are on the one hand dimensionally reduced models (1â\u0080\u0090D\n\tand 0â\u0080\u0090D models, the soâ\u0080\u0090called mixedâ\u0080\u0090dimension model) and on\n\tthe other hand surrogate models produced by kernel methods. Both\n\tmethods are combined in such a way that the mixedâ\u0080\u0090dimension models\n\tyield training data for the surrogate model, where the surrogate\n\tmodel is parametrised by the degree of narrowing of the peripheral\n\tstenosis. By means of a wellâ\u0080\u0090trained surrogate model, we show that\n\tsimulation data can be reproduced with a satisfactory accuracy and\n\tthat parameter optimisation or state estimation problems can be solved\n\tin a very efficient way. Furthermore it is demonstrated that a surrogate\n\tmodel enables us to present after a very short simulation time the\n\timpact of a varying degree of stenosis on blood flow, obtaining a\n\tspeedup of several orders over the full model.", "owner" : "santinge", "groups" : "haasdonk, santin", "eprint" : "https://onlinelibrary.wiley.com/doi/pdf/10.1002/cnm.3095", "file" : ":PDF/KSHH2017_www_preprint.pdf:PDF", "doi" : "10.1002/cnm.3095", "bibtexKey": "KSHH2018" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2832dacbae634d8ae21c67ac44f94850c/mathematik", "tags" : [ "blood","dimensionally","flow","from:mhartmann","ians","kernel","methods,","mixed-dimension","models,","peripheral","real-time","reduced","simulations","simulations,","stenosis,","surrogate","vorlaeufig" ], "intraHash" : "832dacbae634d8ae21c67ac44f94850c", "interHash" : "979f2097ba9c22d67096e59e5c6d7a3e", "label" : "Numerical modelling of a peripheral arterial stenosis using dimensionally\n\treduced models and kernel methods", "user" : "mathematik", "description" : "", "date" : "2018-07-20 10:54:55", "changeDate" : "2019-12-18 14:37:55", "count" : 4, "pub-type": "article", "journal": "International Journal for Numerical Methods in Biomedical Engineering", "year": "2018", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.3095", "author": [ "Tobias Köppl","Gabriele Santin","Bernard Haasdonk","Rainer Helmig" ], "authors": [ {"first" : "Tobias", "last" : "Köppl"}, {"first" : "Gabriele", "last" : "Santin"}, {"first" : "Bernard", "last" : "Haasdonk"}, {"first" : "Rainer", "last" : "Helmig"} ], "volume": "0","number": "ja","pages": "e3095","note": "e3095 cnm.3095","abstract": "Summary In this work, we consider two kinds of model reduction techniquesto\n\tsimulate blood flow through the largest systemic arteries, where\n\ta stenosis is located in a peripheral artery i.e. in an artery that\n\tis located far away from the heart. For our simulations we place\n\tthe stenosis in one of the tibial arteries belonging to the right\n\tlower leg (right post tibial artery). The model reduction techniques\n\tthat are used are on the one hand dimensionally reduced models (1-Dand\n\t0-D models, the so-called mixed-dimension model) and on the other\n\thand surrogate models produced by kernel methods. Both methods are\n\tcombined in such a way that the mixed-dimension models yield training\n\tdata for the surrogate model, where the surrogate model is parametrisedby\n\tthe degree of narrowing of the peripheral stenosis. By means of a\n\twell-trained surrogate model, we show that simulation data can be\n\treproduced with a satisfactory accuracy and that parameter optimisation\n\tor state estimation problems can be solved in a very efficient way.\n\tFurthermore it is demonstrated that a surrogate model enables us\n\tto present after a very short simulation time the impact of a varying\n\tdegree of stenosis on blood flow, obtaining a speedup of several\n\torders over the full model.", "file" : ":http\\://www.mathematik.uni-stuttgart.de/fak8/ians/publications/files/KSHH2017_www_preprint.pdf:PDF", "owner" : "santinge", "doi" : "10.1002/cnm.3095", "bibtexKey": "koppl2018numerical" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2832dacbae634d8ae21c67ac44f94850c/mhartmann", "tags" : [ "blood","dimensionally","flow","kernel","methods,","mixed-dimension","models,","peripheral","real-time","reduced","simulations","simulations,","stenosis,","surrogate","vorlaeufig" ], "intraHash" : "832dacbae634d8ae21c67ac44f94850c", "interHash" : "979f2097ba9c22d67096e59e5c6d7a3e", "label" : "Numerical modelling of a peripheral arterial stenosis using dimensionally\n\treduced models and kernel methods", "user" : "mhartmann", "description" : "", "date" : "2018-07-20 10:54:15", "changeDate" : "2018-07-20 08:54:15", "count" : 4, "pub-type": "article", "journal": "International Journal for Numerical Methods in Biomedical Engineering", "year": "2018", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/cnm.3095", "author": [ "Tobias Köppl","Gabriele Santin","Bernard Haasdonk","Rainer Helmig" ], "authors": [ {"first" : "Tobias", "last" : "Köppl"}, {"first" : "Gabriele", "last" : "Santin"}, {"first" : "Bernard", "last" : "Haasdonk"}, {"first" : "Rainer", "last" : "Helmig"} ], "volume": "0","number": "ja","pages": "e3095","note": "e3095 cnm.3095","abstract": "Summary In this work, we consider two kinds of model reduction techniquesto\n\tsimulate blood flow through the largest systemic arteries, where\n\ta stenosis is located in a peripheral artery i.e. in an artery that\n\tis located far away from the heart. For our simulations we place\n\tthe stenosis in one of the tibial arteries belonging to the right\n\tlower leg (right post tibial artery). The model reduction techniques\n\tthat are used are on the one hand dimensionally reduced models (1-Dand\n\t0-D models, the so-called mixed-dimension model) and on the other\n\thand surrogate models produced by kernel methods. Both methods are\n\tcombined in such a way that the mixed-dimension models yield training\n\tdata for the surrogate model, where the surrogate model is parametrisedby\n\tthe degree of narrowing of the peripheral stenosis. By means of a\n\twell-trained surrogate model, we show that simulation data can be\n\treproduced with a satisfactory accuracy and that parameter optimisation\n\tor state estimation problems can be solved in a very efficient way.\n\tFurthermore it is demonstrated that a surrogate model enables us\n\tto present after a very short simulation time the impact of a varying\n\tdegree of stenosis on blood flow, obtaining a speedup of several\n\torders over the full model.", "file" : ":http\\://www.mathematik.uni-stuttgart.de/fak8/ians/publications/files/KSHH2017_www_preprint.pdf:PDF", "owner" : "santinge", "doi" : "10.1002/cnm.3095", "bibtexKey": "koppl2018numerical" } ] }