Computational Science and Engineering
University of Illinois at Urbana-Champaign

Workshop on

Uncertainty Quantification in Computational Science

June 11-12, 2007

Achieving predictive simulations of physical systems requires a concerted effort in verification, validation, and uncertainty quantification (UQ), including rigorous assessment of model/code validity through comparisons against experimental measurements, with well-characterized uncertainty/error bars for both experimental and computational results. This workshop will present a number of UQ techniques, focusing on generalized polynomial chaos expansions (GPCE) to represent random variables and processes, as well as various techniques for uncertainty propagation in systems governed by ordinary or partial differential equations, with applications in chemistry, thermofluids, materials, etc.

Some specific topics to be addressed include

  • Galerkin modeling in stochastic spaces, including computational solution aspects, error estimation, and post-processing.
  • Non-intrusive (sampling-based) and intrusive (direct) UQ methods.
  • Bayesian methods for estimation of uncertain parameters from data.
  • Current research topics, including interfacing multiscale and stochastic modeling, GPCE and Bayesian based stochastic optimization for stochastic partial differential equations, and UQ for oscillatory dynamical systems.

Principal Lecturers

Dr. Habib N. Najm is a Distinguished Member of the Technical Staff at Sandia National Laboratories in Livermore, CA. He received his Ph.D. in Mechanical Engineering from the Massachusetts Institute of Technology in 1989. His work at Sandia's Combustion Research Facility covers a range of computational reacting flow research, with a focus on development and utilization of advanced algorithms. He also works on the development of stochastic numerical methods for uncertainty quantification in thermofluid systems, on computational studies of stochastic dynamical systems and electrochemical microfluid systems, and on Bayesian statistical techniques for inverse problems. Dr. Najm is author of over fifty archival journal articles and eleven U.S. patents.
Dr. Nicholas J. Zabaras is a Professor at the Sibley School of Mechanical and Aerospace Engineering and Director of the Materials Process Design and Control Laboratory at Cornell University. He received his Ph.D. in Theoretical and Applied Mechanics from Cornell University in 1987. His main research focus is on the development of mathematical, statistical, and stochastic modeling techniques for materials design. He has also contributed extensively on the development of GPCE, stochastic support, sparse grid collocation, and Bayesian techniques for UQ in systems governed by stochastic partial differential equations. His recent work in this area is on data-driven, reduced-order stochastic models for topological uncertainty. Dr. Zabaras is author of over ninety archival journal articles.
For more information on the workshop, see the website http://www.cse.uiuc.edu/uq/.
To register for the workshop, send email to uq_workshop@cse.uiuc.edu.