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DTSTART;TZID=America/Los_Angeles:20190208T111000
DTEND;TZID=America/Los_Angeles:20190208T120000
DTSTAMP:20260612T023046
CREATED:20190208T171147Z
LAST-MODIFIED:20190208T171147Z
UID:63927-1549624200-1549627200@tricities.wsu.edu
SUMMARY:CE Seminar Series - Uncertainty quantification and reduction with conditional Gaussian process models in high dimensional stochastic systems
DESCRIPTION:Ramakrishna Tipireddy\, Ph.D.\nResearch Scientist in the Physical and Computational Sciences Directorate \nPacific Northwest National Laboratory (PNNL) \n  \nAbstract\nThis talk will present a brief introduction to uncertainty quantification (UQ) quantification methods for\nhigh dimensional stochastic PDEs and introduce conditional Gaussian process (GP) models for uncertainty\nreduction. The PDE coefficient is represented as a log-normal random field\, with the corresponding\nGaussian part modeled as a zero-mean Gaussian process (GP) with appropriate covariance function. The\nreduction in uncertainty is achieved by conditioning the GP model on observations of the coefficient at a\nfew spatial locations. The resulting conditional GP model is then discretized using truncated Karhunen-\nLo`eve (KL) expansion and the stochastic solution of the PDE is computed using Monte Carlo and sparsegrid\ncollocation methods. Finally\, uncertainty in the system is further reduced by adaptively selecting\nadditional observation locations using two active learning criteria. The first criteria minimizes the\nvariance of the PDE coefficient\, while the second criteria employs approximately minimizes the variance\nof the solution of the PDE.\nBio\nThis talk will present a brief introduction to uncertainty quantification (UQ) quantification methods for\nhigh dimensional stochastic PDEs and introduce conditional Gaussian process (GP) models for uncertainty\nreduction. The PDE coefficient is represented as a log-normal random field\, with the corresponding\nGaussian part modeled as a zero-mean Gaussian process (GP) with appropriate covariance function. The\nreduction in uncertainty is achieved by conditioning the GP model on observations of the coefficient at a\nfew spatial locations. The resulting conditional GP model is then discretized using truncated Karhunen-\nLo`eve (KL) expansion and the stochastic solution of the PDE is computed using Monte Carlo and sparsegrid\ncollocation methods. Finally\, uncertainty in the system is further reduced by adaptively selecting\nadditional observation locations using two active learning criteria. The first criteria minimizes the\nvariance of the PDE coefficient\, while the second criteria employs approximately minimizes the variance\nof the solution of the PDE.\nCivil
URL:/event/ce-seminar-series-uncertainty-quantification-and-reduction-with-conditional-gaussian-process-models-in-high-dimensional-stochastic-systems/
LOCATION:BSEL 103
CATEGORIES:academic,Calendar,Event,Professional Development
ORGANIZER;CN="Srinivas Allena":MAILTO:srinivas.allena@wsu.edu
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