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CATEGORIES:Academics,Lectures/Seminars
DESCRIPTION:ME-EM Graduate Seminar Speaker Series\n\nproudly presents\n\nRa
makrishna Tipireddy\, PhD\n\nPacific Northwest National Laboratory\n\nAbstr
act\n\nThis talk will present a brief introduction to polynomial chaos (PC)
based uncertainty quantification (UQ) methods for high dimensional stochas
tic partial differential equations (SPDEs) and introduce stochastic dimensi
on reduction and spatial domain decomposition methods. This talk also prese
nts conditional Gaussian process (GP) models for uncertainty reduction. In
this approach\, the PDE coefficient is represented as a log-normal random f
ield\, with the corresponding Gaussian part modeled as a zero-mean Gaussian
process (GP) with appropriate covariance function. The reduction in uncert
ainty is achieved by conditioning the GP model on observations of the coeff
icient at a few spatial locations. The resulting conditional GP model is th
en discretized using truncated Karhunen-Loève (KL) expansion and the stocha
stic solution of the PDE is computed using Monte Carlo and sparse-grid coll
ocation methods. Uncertainty in the system is further reduced by adaptively
selecting additional observation locations using two active learning crite
ria. The talk will feature several applications in computational mechanics
such as random eigenvalue analysis for stability of a wind turbine blade wi
th random Young’s modulus\, plate with a hole subjected to internal pressur
e.\n\nBio\n\nRamakrishna Tipireddy is a Research Scientist in the Physical
and Computational Sciences Directorate at the Pacific Northwest National La
boratory. His research interests include uncertainty quantification\, compu
tational mechanics\, and development of reduced order models for complex st
ochastic systems. Tipireddy received his PhD in civil engineering from Univ
ersity of Southern California.\n\nInvited by: Susanta Ghosh
DTEND:20220217T220000Z
DTSTAMP:20240803T223225Z
DTSTART:20220217T210000Z
LOCATION:
SEQUENCE:0
SUMMARY:Uncertainty Quantification\, Dimension Reduction and Domain Decompo
sition for High Dimensional Stochastic Partial Differential Equations
UID:tag:localist.com\,2008:EventInstance_39173154120190
URL:https://events.mtu.edu/event/uncertainty_quantification_dimension_reduc
tion_and_domain_decomposition_for_high_dimensional_stochastic_partial_diffe
rential_equations
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