Uncertainty Quantification: Theory, Implementation, and Applications: 30

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.