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Uncertainty, validation of computer models and the myth of numerical predictability.

DE2001780698

Publication Date	2001
Personal Author	Hemez, F. M.
Page Count	51
Abstract	This publication addresses the issues of modeling, uncertainly quantification, model validation and numerical predictability. With the increasing role of numerical simulation in science, technology as well as every day decision-making, assessing the predictive accuracy of computer models becomes essential. Conventional approaches such as finite element model updating or Bayesian inference are undeniably useful tools but they do not fully answer the question: How accurately does the model represent reality. First, the evolution of scientific computing and consequences in terms of modeling and analysis practices are discussed. The intimate relationship between modeling and uncertainly is explored by defining uncertainly as an integrate part of the model, not just parametric variability or the lack of knowledge about the physical system being investigated. Examples from nuclear physics, climate prediction and structural dynamics are provided to illustrate issues related to uncertainly, validation and predictability. Feature extraction or the characterization of the dynamics of interest from time series is also discussed. Finally, a general framework based on response surface methodology is proposed for the fusion of model predictions, validation data sets and uncertainly analysis.
Keywords	Computer models Modeling Uncertainty quantification Model validation Numerical predictability
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	62 - Computers, Control & Information Theory 46 - Physics
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200210
Contract Number	W-7405-ENG-36

Uncertainty, validation of computer models and the myth of numerical predictability.

Uncertainty, validation of computer models and the myth of numerical predictability.
DE2001780698

Publication Date	2001
Personal Author	Hemez, F. M.
Page Count	51
Abstract	This publication addresses the issues of modeling, uncertainly quantification, model validation and numerical predictability. With the increasing role of numerical simulation in science, technology as well as every day decision-making, assessing the predictive accuracy of computer models becomes essential. Conventional approaches such as finite element model updating or Bayesian inference are undeniably useful tools but they do not fully answer the question: How accurately does the model represent reality. First, the evolution of scientific computing and consequences in terms of modeling and analysis practices are discussed. The intimate relationship between modeling and uncertainly is explored by defining uncertainly as an integrate part of the model, not just parametric variability or the lack of knowledge about the physical system being investigated. Examples from nuclear physics, climate prediction and structural dynamics are provided to illustrate issues related to uncertainly, validation and predictability. Feature extraction or the characterization of the dynamics of interest from time series is also discussed. Finally, a general framework based on response surface methodology is proposed for the fusion of model predictions, validation data sets and uncertainly analysis.
Keywords	Computer models Modeling Uncertainty quantification Model validation Numerical predictability
Source Agency	Technical Information Center Oak Ridge Tennessee
NTIS Subject Category	62 - Computers, Control & Information Theory 46 - Physics
Corporate Authors	Los Alamos National Lab., NM.; Department of Energy, Washington, DC.
Document Type	Conference Proceedings
NTIS Issue Number	200210
Contract Number	W-7405-ENG-36