Numerical Methods for Uncertainty Quantification

Date: 13-17 May 2013

Venue: Mathematik-Zentrum, Lipschitz Lecture Hall, Endenicher Allee 60, Bonn

Organizers: Alexey Chernov (HCM, Univ. Bonn), Vincent Heuveline (Univ. Heidelberg / HITS), Fabio Nobile (EPF Lausanne)

The workshop aims at showcasing different aspects related to Uncertainty Quantification in differential models and the most recent and important progresses in the field both at the theoretical and computational level. The workshop will bring together the leading scientists and active young researchers working on Numerical Methods for Uncertainty Quantification in various fields and initiate an intensive idea exchange between the research fields.

The topics of the workshop include but are not limited to

  • Forward uncertainty propagation
  • Uncertainty propagation in time dependent problems
  • Reduced order models and low rank approximations
  • Inverse uncertainty characterization
  • Optimization and optimal control problems under uncertainty

Computational Science and Engineering has emerged in the last years as an extremely powerful discipline able to simulate the behavior and evolution of complex systems described by sophisticated multiscale / multiphysics mathematical models. All this is made possible by the exponential growth of computer power that we have witnessed in the last decades. With the increasing use of computational tools, comes also an increasing need of a systematic assessment of the reliability of computer simulations and quantification of all sources of uncertainties associated to them, including discretization errors, models inadequacy, incomplete knowledge of model parameters, etc.

Uncertainty Quantification (UQ) in Computational Science and Engineering is a relatively new area aiming at developing theory and methods to quantitatively describe the origin, propagation, and interplay of different sources of error and uncertainty in the analysis and prediction of the behavior of complex systems, arising for instance in engineering, biology, chemistry, geophysics, life sciences, finance, etc. UQ bridges several disciplines including statistics, numerical analysis, computational sciences.