Publisher's Synopsis
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.
