High Dimensional Approximation for Uncertainty Quantification - November 10, 2011

This workshop was held at SAMSI.

Motivation and Goals

The field of uncertainty quantification (UQ) has undergone tremendous growth in recent years. Many new methods and algorithms have been developed and used successfully to understand uncertainties in the computational simulation of large and complex problems. Though significant progress has been made,the curse of dimensionality remains a long-standing and cross-cutting challenge. Solving UQ problems(including uncertainty propagation, parameter estimation, probabilistic prediction and design) in high-dimensional parameter spaces requires efficient approaches to high-dimensional approximation. A host of ideas and techniques are relevant to this problem, including sparse grids, low-rank tensor approximations,l1 minimization, nonparametric regression, error estimation and adaptivity, and experimental design.

The purpose of this workshop was to bring together a group of researchers, who have made notable contributions to this challenge, to present and compare different strategies and to discuss new ideas. A particular emphasis of the workshop was to bridge gaps of viewpoint and methodology between applied mathematicians and statisticians, and to foster intellectual interactions and cross-disciplinary collaborations.

Format

Participation was open for a limited number of external registrants. External speakers, listed below, presented their views and research on problems of high-dimensional approximation in UQ. Group discussions were carried out throughout the day to encourage interaction and a deeper understanding of the research issues.

Schedule

Wednesday, November 09, 2011
at SAMSI

Thursday, November 10, 2011
at SAMSI