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DTSTART;TZID=America/Detroit:20180406T150000
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SUMMARY:CEE/MICDE Seminar: Khachik Sargsyan\, Sandia National Laboratories
DESCRIPTION:Bio: Khachik Sargsyan is a Principal Member of Technical Staff at Sandia National Laboratories (SNL) in Livermore\, CA. Before staff and postdoctoral positions at SNL\, he received his Ph.D. in Applied and Interdisciplinary Mathematics from University of Michigan\, Ann Arbor\, in 2007. His Bachelors degree\, awarded in 2002\, is in Applied Math and Physics from Moscow Institute of Physics and Technology. Dr. Sargsyan’s research evolves around uncertainty quantification (UQ) and predictability analysis of physical and computational models. He has developed and applied methods for model reduction\, UQ and data assimilation\, targeting fundamental challenges such as structural errors\, intrinsic stochasticity\, high-dimensionality\, limited data\, discontinuities and rare events\, with applications in climate modeling\, chemical kinetics\, hardware architecture simulators and turbulent combustion. He is one of the lead developers of UQTk (www.sandia.gov/uqtoolkit)\, a lightweight C++/Python software toolkit for quantification of uncertainties in model predictions.\n \nDr. Sargsyan is being hosted by Prof. Ivanov (Civil and Env. Engineering). If you would like to meet him\, please send an email to Chase Dwelle at dwellem@umich.edu \nProbabilistic Methods for Uncertainty Quantification in Computational Models\nOver the last decade\, improved measurement capabilities and computational resources have led to significant algorithmic developments toward efficient uncertainty quantification (UQ) for computational models. Such models of physical systems often involve input parameters that exhibit certain degree of uncertainty. Estimation and propagation of these uncertainties are crucial for model validation\, computational/experimental design and decision making. ​This talk will focus on probabilistic methods with emphasis on Polynomial Chaos (PC) expansions as a means for functional representation of random variables. The talk will highlight the use of PC methods both for forward propagation of uncertainties and for inverse problems\, such as parameter estimation via Bayesian inference. I will list associated major challenges\, including the curse of dimensionality and model structural error estimation\, in the context of computationally expensive models of physical systems. Both fundamental and more recent methods will be introduced and demonstrated\, impacting a wide range of applications\, such as climate modeling\, turbulent combustion and chemical kinetics.
URL:https://micde.umich.edu/event/cee-micde-seminar-khachik-sargsyan-sandia-national-laboratories/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series,Seminar
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SUMMARY:AIM Seminar: Christoph Börgers\, Mathematics\, Tufts University
DESCRIPTION:Bio: Christoph Börgers is a Professor of Mathematics at Tufts University. He got his Ph.D. under Prof. Charles Peskin at the Courant Institute of Mathematical Sciences\, in 1985. Prof. Börgers was a professor in the University of Michigan department of Mathematics until 1996 when he moved to Tufts. His expertise is in mathematical neuroscience\, applied dynamical systems\, numerical analysis\, scientific computing\, and during the past decade\, most of his work has been in the area of Computational Neuroscience. \nRhythms in neuronal networks with recurrent excitation\nInteracting excitatory and inhibitory neuronal populations often generate oscillations in electrical fields in the brain. I will briefly review this mechanism and the reasons to believe that it is important in brain function. Most of the talk will be focused on the effects of recurrent excitation\, i.e.\, of the neurons of a local network in the brain exciting each other. Recurrent excitation can sustain activity in a network that would otherwise be quiescent; this is believed to be the basis of working memory. It can also lead to a runaway process\, with excitation generating more excitation etc.\, much as the presence of a quadratic term on the right-hand side of a differential equation can lead to blow-up in finite time; this may be related to epileptic seizures. For model problems\, we prove that abrupt transitions to runaway activity require recurrent excitation with fast kinetics\, while working memory activity is more robust with recurrent excitation with slow kinetics. \nProf. Börgers is being hosted by Prof. Robert Krasny (Mathematics).
URL:https://micde.umich.edu/event/aim-seminar-christoph-borgers-mathematics-tufts-university/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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