{"420ba292a23e5ebfcab148983443645dishaandesai":{"DOI":"","ISBN":"","ISSN":"","URL":"","abstract":"","annote":"","author":[{"family":"Maier","given":"Benjamin"},{"family":"Schneider","given":"David"},{"family":"Schulte","given":"Miriam"},{"family":"Uekermann","given":"Benjamin"}],"citation-label":"maier2021bridging","collection-editor":[],"collection-title":"","container-author":[],"container-title":"High Performance Computing in Science and Engineering '21: Transactions of the High Performance Computing Center, Stuttgart (HLRS) 2021","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2021"]],"literal":"2021"},"event-place":"","id":"420ba292a23e5ebfcab148983443645dishaandesai","interhash":"9fb09a978da1a166339ebf879e42cf12","intrahash":"420ba292a23e5ebfcab148983443645d","issue":"","issued":{"date-parts":[["2021"]],"literal":"2021"},"keyword":"pn5 simulations precice exc2075","note":"","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Bridging Scales with Volume Coupling --- Scalable Simulations of Muscle Contraction and Electromyography","type":"chapter","username":"ishaandesai","version":"","volume":""},"5f74ed6faec00b5ef556cb54cc6d0c07ishaandesai":{"DOI":"","ISBN":"","ISSN":"","URL":"","abstract":"The coupling library preCICE allows to couple single-physics solvers to partitioned\r\nmulti-physics simulations in a black-box fashion. preCICE is a C++ library, but it offers\r\nlanguage bindings to access the preCICE API from solvers written in other languages,\r\nsuch as C, Python, Fortran and MATLAB. The Julia Programming Language designed\r\nfor numerical computing is a strong candidate to be supported by preCICE. While Julia\r\nprovides a wide set of tools for interfacing with other languages, including C++, porting\r\na library such as preCICE that is made for High Performance Computing and runs on a\r\nhuge number of processes, requires little to no compromises. Multiple ways of wrapping\r\na C/C++ library are presented and implemented. In addition come Julia’s own features,\r\nfor example the Distributed base library, that deviate from classic standards of known\r\nscientific languages. To test the bindings, two dummy solvers are coupled and presented in an example setup, with an outlook on further development.","annote":"","author":[{"family":"Kharitenko","given":"Pavel"}],"citation-label":"kharitenko2021coupling","collection-editor":[],"collection-title":"","container-author":[],"container-title":"","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2021","10"]],"literal":"2021"},"event-place":"","id":"5f74ed6faec00b5ef556cb54cc6d0c07ishaandesai","interhash":"3e8d3b3b9b2100facb8d5fdbb08f1884","intrahash":"5f74ed6faec00b5ef556cb54cc6d0c07","issue":"","issued":{"date-parts":[["2021","10"]],"literal":"2021"},"keyword":"coupling julia simulations precice","note":"","number":"","page":"","page-first":"","publisher":"","publisher-place":"","status":"","title":"Coupling Julia-based Simulations via preCICE","type":"article","username":"ishaandesai","version":"","volume":""},"832dacbae634d8ae21c67ac44f94850cmhartmann":{"DOI":"10.1002/cnm.3095","ISBN":"","ISSN":"","URL":"https://onlinelibrary.wiley.com/doi/abs/10.1002/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.","annote":"","author":[{"family":"Köppl","given":"Tobias"},{"family":"Santin","given":"Gabriele"},{"family":"Haasdonk","given":"Bernard"},{"family":"Helmig","given":"Rainer"}],"citation-label":"koppl2018numerical","collection-editor":[],"collection-title":"","container-author":[],"container-title":"International Journal for Numerical Methods in Biomedical Engineering","documents":[],"edition":"","editor":[],"event-date":{"date-parts":[["2018"]],"literal":"2018"},"event-place":"","id":"832dacbae634d8ae21c67ac44f94850cmhartmann","interhash":"979f2097ba9c22d67096e59e5c6d7a3e","intrahash":"832dacbae634d8ae21c67ac44f94850c","issue":"ja","issued":{"date-parts":[["2018"]],"literal":"2018"},"keyword":"models, peripheral kernel simulations reduced mixed-dimension methods, blood simulations, stenosis, real-time dimensionally surrogate vorlaeufig flow","misc":{"file":":http\\://www.mathematik.uni-stuttgart.de/fak8/ians/publications/files/KSHH2017_www_preprint.pdf:PDF","owner":"santinge","doi":"10.1002/cnm.3095"},"note":"e3095 cnm.3095","number":"ja","page":"e3095","page-first":"3095","publisher":"","publisher-place":"","status":"","title":"Numerical modelling of a peripheral arterial stenosis using dimensionally\n\treduced models and kernel methods","type":"article-journal","username":"mhartmann","version":"","volume":"0"}}