{"63872":{"#nid":"63872","#data":{"type":"event","title":"CSE Seminar: Youssef Marzouk","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EYoussef Marzouk\u003C\/strong\u003E\u003Cbr \/\u003EMIT, Department of Aeronautics and Astronautics\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0022Algorithms for inference and experimental design in complex physical systems\u0022\u003Cbr \/\u003E\u0026nbsp;\u003Cbr \/\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003ESimulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference and for the optimal selection of experiments and observations. Computationally intensive models, however, can render a Bayesian approach prohibitive.\u003C\/p\u003E\u003Cp\u003EWe will show that stochastic spectral methods, which have seen extensive development in the context of \u0022forward\u0022 uncertainty propagation, are a useful tool for inference as well. We introduce a stochastic spectral formulation that accelerates Bayesian inference via rapid exploration of a surrogate posterior distribution. Theoretical convergence results are verified with several numerical examples---in particular, parameter estimation in transport processes and in chemical kinetic systems. We also extend this approach to high-dimensional and ill-posed inverse problems, estimating distributed quantities in a hierarchical Bayesian setting.\u003C\/p\u003E\u003Cp\u003EWe will also discuss computational strategies for optimal experimental design---choosing experimental conditions to maximize information gain in parameters or outputs of interest. We propose a general Bayesian framework for experimental design with nonlinear simulation-based models, accounting for uncertainty in model parameters, experimental conditions, and observables. We then discuss efficient evaluation of the associated objective functions, coupled with stochastic optimization methods to maximize expected utility.\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:ymarz@mit.edu\u0022\u003Eymarz@mit.edu\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E~~~~~~~~~~~~~~~~\u003C\/p\u003E\u003Cp\u003ETo receive future announcements, please sign up to the cse-seminar email list:\u003Cbr \/\u003E\u003Ca href=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/cse-seminar\u0022 title=\u0022https:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/cse-seminar\u0022\u003Ehttps:\/\/mailman.cc.gatech.edu\/mailman\/listinfo\/cse-seminar\u003C\/a\u003E\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Algorithms for inference and experimental design in complex physical systems"}],"uid":"27154","created_gmt":"2011-01-26 10:08:49","changed_gmt":"2016-10-08 01:53:56","author":"Louise Russo","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2011-01-28T13:00:00-05:00","event_time_end":"2011-01-28T14:00:00-05:00","event_time_end_last":"2011-01-28T14:00:00-05:00","gmt_time_start":"2011-01-28 18:00:00","gmt_time_end":"2011-01-28 19:00:00","gmt_time_end_last":"2011-01-28 19:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"37041","name":"Computational Science and Engineering"},{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[],"keywords":[{"id":"3497","name":"cse seminar"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EGeorge Biros\u003C\/p\u003E\u003Cp\u003E\u003Ca href=\u0022mailto:gbiros@cc.gatech.edu\u0022\u003Egbiros@cc.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}