Type: 
General Event
Date-Time: 
Thursday, March 12, 2015 - 16:00 to 17:50
Location: 
Joyce Powell Leadership Room, Memorial Union
Event Speaker: 
Prof. Juan M. Restrepo, Department of Mathematics
Local Contact: 
Jansen
Abstract: 

Accounting for uncertainties has led us to alter our expectations of what is
predictable and how such predictions compare to nature. A significant effort, in
recent years, has been placed on creating new uncertainty quantification
techniques, rediscovering old ones, and the appropriation of existing ones to
account for uncertainties in modeling and simulations.
Is this nothing more than a greater reliance on statistics techniques in our regular
business? Some of it is. However, as this presentation will recount and illustrate,
there are important changes on how we perform the business of modeling and
predicting natural phenomena: Bayesian inference is used to combine models and
data (not just to compare models and data); sensitivity analyses and projection
techniques influence mean-field modeling; data classification techniques allow us
to work with the more general state variables, which subsume dynamic physical
variables; we exploit complex stochastic representations to better capture multiscale
phenomena or to capture the small-scale correlations of big data sets.