Accession Number ADA562693
Title Uncertainty Quantification for Systems Governed by Partial Differential Equations. Workshop report.
Publication Date Jun 2012
Media Count 18p
Personal Author A. Cliffe C. Powell M. Gunzburger P. Houston
Abstract In deterministic mathematical modeling, 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 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 encompasses many tasks that are crucial to both public and private enterprise. This includes, crucially, risk assessments that are typically used to inform policy makers e.g. on sensitive issues such as the safety of a nuclear waste repository. It has long been recognized that mathematical models need to account for uncertainty but progress has been hampered by a lack of mathematical analysis and computing resources. The science of UQ has been in its infancy in many application areas until relatively recently but is now rapidly developing. The workshop brought together numerical analysts, probabilists, computer scientists and industrialists (amongst whom there is traditionally very little communication) to disseminate important advances currently being made in sparse high-dimensional sampling, high-dimensional quadrature, approximation theory, model-order reduction, sensitivity analysis, statistics and scientific computing.
Keywords Deterministic mathematical modeling
Foreign reports
Knowledge transfer
Mathematical models
Numerical methods and procedures
Partial differential equations
Uncertainty
Uncertainty quantification
United kingdom
Workshops


 
Source Agency Non Paid ADAS
NTIS Subject Category 72B - Algebra, Analysis, Geometry, & Mathematical Logic
72F - Statistical Analysis
72E - Operations Research
Corporate Author Heriot-Watt Univ., Edinburgh (Scotland).
Document Type Technical report
Title Note Conference Proceedings.
NTIS Issue Number 1225
Contract Number FA8655-10-1-5079

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