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 high-dimensional 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 high-dimensional 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.