Uncertainty Quantification

Theory, Implementation, and Applications
Uncertainty quantification is an important step in establishing the predictive accuracy of simulation models employed in a broad range of disciplines. The book provides a comprehensive and unified treatment of the mathematical, statistical, and numerical topics required to perform uncertainty analysis for models arising in a range of applications.
Uncertainty quantification is an important step in establishing the predictive accuracy of simulation models employed in a broad range of disciplines. The book provides a comprehensive and unified treatment of the mathematical, statistical, and numerical topics required to perform uncertainty analysis for models arising in a wide range of applications.

Expanded and reorganized, the second edition represents advances in the field over the last decade.

• It contains new chapters on random field representations, observation models, parameter identifiability and influence, active subspace techniques, and statistical surrogate models.
• The chapter on local sensitivity analysis has been rewritten to focus on the use of sensitivity equations, complex-step approximation, adjoint methods, and parameter subset selection techniques to ascertain parameter influence.
• It contains four times the number of exercises and many new examples, several of which include data.
• UQ Crimes throughout the text identify common misconceptions and guide readers entering the field.
• An ancillary website contains MATLAB codes.

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