Uncertainty Quantification

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Uncertainty Quantification

 24 - 28 May 2010

Royal Society of Edinburgh

  • Andrew Cliffe, University of Nottingham
  • Max Gunzburger, Florida State University
  • Paul Houston, University of Nottingham
  • Catherine Powell, University of Manchester


In deterministic modelling, complete knowledge of input parameters is assumed; this leads to simplified, tractable computations and produces simulations of outputs that correspond to specific choices of inputs. However, most physical, biological, social, economic and financial processes, etc, involve some degree of uncertainty. Uncertainty quantification (UQ) is the task of determining statistical information about the outputs of a process of interest, given only statistical (i.e., incomplete) information about the inputs. It has long been recognised that mathematical models need to account for uncertainty. The science of UQ has been in its infancy in any application areas until relatively recently but is now rapidly developing.  This workshop will concentrate on UQ for processes that are governed by partial differential equations (PDEs).

A complete list of speakers and their talk titles

Sponsors and Funders:

We wish to thank the following organisations for their kind support: ICMS, LMS, the US National Science Foundation and the European Office of Aerospace Research and Development, the Air Force Office of Scientific Research and the United States Air Force R