{"662311":{"#nid":"662311","#data":{"type":"event","title":"ISyE Statistics Seminar Speaker- Ralph C. Smith, North Carolina State University ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EFor many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation.\u0026nbsp; For example, nuclear power plant codes can take hours to days for a single run.\u0026nbsp; Furthermore, the models often have tens to thousands of inputs -- comprised of parameters, initial conditions, or boundary conditions -- many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs for subsequent surrogate model construction for Bayesian inference and uncertainty propagation.\u0026nbsp; For input selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and detail the use of parameter subset selection and active subspace techniques to determine low-dimensional input spaces.\u0026nbsp; We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters.\u0026nbsp; These techniques will be illustrated for models arising in nuclear power plant design and quantitative systems pharmacology (QSP), as well as models for transductive materials.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBiography:\u003C\/strong\u003E Ralph C. Smith joined the North Carolina State University faculty in 1998 where he is presently a Distinguished University Professor of Mathematics.\u0026nbsp; He is co-author of the research monograph \u003Cem\u003ESmart Material Structures: Modeling, Estimation and Control\u003C\/em\u003E and author of the books \u003Cem\u003ESmart Material Systems: Model Development\u003C\/em\u003E and \u003Cem\u003EUncertainty Quantification: Theory, Implementation, and Applications\u003C\/em\u003E.\u0026nbsp; He is on the editorial boards of the \u003Cem\u003EJournal of Intelligent Material Systems and Structures\u003C\/em\u003E and the \u003Cem\u003ESIAM\/ASA Journal on Uncertainty Quantification\u003C\/em\u003E. He is the recipient of the 2016 ASME \u003Cem\u003EAdaptive Structures and Material Systems Prize \u003C\/em\u003Eand the SPIE 2017 \u003Cem\u003ESmart Structures and Materials Lifetime Achievement, \u003C\/em\u003Eand he was named a \u003Cem\u003ESIAM Fellow\u003C\/em\u003E in 2018\u003Cem\u003E. \u003C\/em\u003EHis research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification for physical and biological systems.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract: \u003C\/strong\u003EFor many complex physical and biological models, the computational cost of high-fidelity simulation codes precludes their direct use for Bayesian model calibration and uncertainty propagation.\u0026nbsp; For example, nuclear power plant codes can take hours to days for a single run.\u0026nbsp; Furthermore, the models often have tens to thousands of inputs -- comprised of parameters, initial conditions, or boundary conditions -- many of which are unidentifiable in the sense that they cannot be uniquely determined using measured responses. In this presentation, we will discuss techniques to isolate influential inputs for subsequent surrogate model construction for Bayesian inference and uncertainty propagation.\u0026nbsp; For input selection, we will discuss advantages and shortcomings of global sensitivity analysis to isolate influential inputs and detail the use of parameter subset selection and active subspace techniques to determine low-dimensional input spaces.\u0026nbsp; We will also discuss the manner in which Bayesian calibration on active subspaces can be used to quantify uncertainties in physical parameters.\u0026nbsp; These techniques will be illustrated for models arising in nuclear power plant design and quantitative systems pharmacology (QSP), as well as models for transductive materials.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBiography:\u003C\/strong\u003E Ralph C. Smith joined the North Carolina State University faculty in 1998 where he is presently a Distinguished University Professor of Mathematics.\u0026nbsp; He is co-author of the research monograph \u003Cem\u003ESmart Material Structures: Modeling, Estimation and Control\u003C\/em\u003E and author of the books \u003Cem\u003ESmart Material Systems: Model Development\u003C\/em\u003E and \u003Cem\u003EUncertainty Quantification: Theory, Implementation, and Applications\u003C\/em\u003E.\u0026nbsp; He is on the editorial boards of the \u003Cem\u003EJournal of Intelligent Material Systems and Structures\u003C\/em\u003E and the \u003Cem\u003ESIAM\/ASA Journal on Uncertainty Quantification\u003C\/em\u003E. He is the recipient of the 2016 ASME \u003Cem\u003EAdaptive Structures and Material Systems Prize \u003C\/em\u003Eand the SPIE 2017 \u003Cem\u003ESmart Structures and Materials Lifetime Achievement, \u003C\/em\u003Eand he was named a \u003Cem\u003ESIAM Fellow\u003C\/em\u003E in 2018\u003Cem\u003E. \u003C\/em\u003EHis research areas include mathematical modeling of smart material systems, numerical analysis and methods for physical systems, Bayesian model calibration, sensitivity analysis, control, and uncertainty quantification for physical and biological systems.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Active Subspace Techniques to Construct Surrogate Models for Complex Simulation Codes"}],"uid":"36358","created_gmt":"2022-10-19 15:03:08","changed_gmt":"2022-10-19 15:03:08","author":"chumphrey30","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2022-11-03T13:00:00-04:00","event_time_end":"2022-11-03T14:00:00-04:00","event_time_end_last":"2022-11-03T14:00:00-04:00","gmt_time_start":"2022-11-03 17:00:00","gmt_time_end":"2022-11-03 18:00:00","gmt_time_end_last":"2022-11-03 18:00:00","rrule":null,"timezone":"America\/New_York"},"extras":["free_food"],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"78771","name":"Public"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}