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X-WR-CALNAME:Michigan Institute for Computational Discovery and Engineering
X-ORIGINAL-URL:https://micde.umich.edu
X-WR-CALDESC:Events for Michigan Institute for Computational Discovery and Engineering
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DTSTAMP:20260605T152523
CREATED:20230905T171445Z
LAST-MODIFIED:20230905T171445Z
UID:10000017-1677771000-1677774600@micde.umich.edu
SUMMARY:MICDE Seminar: Daniele Schiavazzi\, Associate Professor of Applied and Computational Mathematics and Statistics\, University of Notre Dame
DESCRIPTION:Daniele Schiavazzi is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He graduated with honors and received a Ph.D. in Applied Mathematics from Universita’ degli Studi di Padova in Italy. He held postdoctoral appointments at the University of California\, San Diego and Stanford University. He is the recipient of a CAREER Award from the National Science Foundation\, a Young Faculty Award from DARPA and a Postdoctoral Fellowship from the American Heart Association. His research interests include uncertainty quantification\, cardiovascular simulation\, multi-resolution and multi-fidelity approximation\, model-based inference and inverse problems in medical imaging. \nTalk Title: NEW PARADIGMS FOR ENSEMBLE MODELING\, UNCERTAINTY QUANTIFICATION AND INFERENCE IN CARDIOVASCULAR SIMULATION \nAbstract: \nComputer simulations are increasingly used to complement clinical decision making in the diagnosis and treatment of cardiovascular disease. High-fidelity cardiovascular models are traditionally deterministic and solved using implicit time integration\, without directly accounting for uncertainty and variability in the underlying input processes\, for example boundary conditions\, material properties or segmented model anatomy. I will discuss an alternative simulation paradigm based on the explicit integration in time of an ensemble of model realizations\, running on multiple GPUs. Additionally\, I will present some results on the acceleration of traditional numerical solvers through data-driven methods based on deep neural networks\, focusing on synchronization-avoiding algorithms for distributed finite element solvers. I will finally discuss recently proposed approaches for multi-fidelity uncertainty propagation and variational inference\, combining high-fidelity cardiovascular models with their low-fidelity approximation or neural network surrogate. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Schiavazzi will be hosted by Prof. Alex Gorodetsky\, Assistant Professor of Aerospace Engineering. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-daniele-schiavazzi-associate-professor-of-applied-and-computational-mathematics-and-statistics-university-of-notre-dame/
LOCATION:1014 H. H. Dow\, 2300 Hayward St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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