SS114 AIMS 2016 Meeting, Orlando, Florida, USA
Title: Uncertainty Quantification 
Organizer(s): 
Name:

Affiliation: 
Country: 
Email Address: 
Kody Law

Oak Ridge National Laboratory

USA

kodylaw@gmail.com

Clayton Webster

Oak Ridge National Laboratory

USA

webstercg@ornl.gov


Introduction:
Uncertainties in the parameters which define differential equations give rise to distributions of solutions, hence distributions of functionals of the solutions. Quantities of interest can be represented as expectations of such functionals. The resulting highdimensional integrals, involving expensive function evaluations, has lead to a wealth of new Mathematics. Further complexity is
introduced if data is available. The Bayesian framework gives rise to a probabilistic interpretation of inverse problems. This special session aims to bring together researchers in highdimensional approximation theory, numerical and computational methods for stochastic partial differential equations, and Bayesian inverse problems to share ideas and discuss these interesting problems and the interplay between them. 

List of approved abstract 


