Uncertainty quantification : theory, implementation, and applications / Ralph Smith, North Carolina State University, Raleigh, North Carolina.

Format:

Book

Author/Creator:

Smith, Ralph C., 1960- author.

Contributor:

Society for Industrial and Applied Mathematics, publisher.

Series:

Computational science and engineering series.

Computational science and engineering series

Language:

English

Subjects (All):

Measurement uncertainty (Statistics).

Estimation theory.

Physical Description:

1 online resource (xviii, 382 pages) : illustrations.

Place of Publication:

Philadelphia, Pennsylvania : Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), 2013.

System Details:

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Summary:

The field of uncertainty quantification is evolving rapidly because of increasing emphasis on models that require quantified uncertainties for large-scale applications, novel algorithm development, and new computational architectures that facilitate implementation of these algorithms. Uncertainty Quantification: Theory, Implementation, and Applications provides readers with the basic concepts, theory, and algorithms necessary to quantify input and response uncertainties for simulation models arising in a broad range of disciplines. The book begins with a detailed discussion of applications where uncertainty quantification is critical for both scientific understanding and policy. It then covers concepts from probability and statistics, parameter selection techniques, frequentist and Bayesian model calibration, propagation of uncertainties, quantification of model discrepancy, surrogate model construction, and local and global sensitivity analysis. The author maintains a complementary web page where readers can find data used in the exercises and other supplementary material. Uncertainty Quantification: Theory, Implementation, and Applications includes a very large number of definitions and examples that use a suite of relatively simple models to illustrate concepts; numerous references to current and open research issues; exercises that illustrate basic concepts and guide readers through the numerical implementation of algorithms for prototypical problems; a wide range of applications, including weather and climate models, subsurface hydrology and geology models, nuclear power plant design, and models for biological phenomena; and recent advances and topics that have appeared in the research literature within the last 15 years, including aspects of Bayesian model calibration, surrogate model development, parameter selection techniques, and global sensitivity analysis.

Contents:

Preface

Notation

Acronyms and initialisms

Introduction

Large-scale applications

Prototypical models

Fundamentals of probability, random processes, and statistics

Representation of random inputs

Parameter selection techniques

Frequentist techniques for parameter estimation

Bayesian techniques for parameter estimation

Uncertainty propagation in models

Stochastic spectral methods

Sparse grid quadrature and interpolation techniques

Prediction in the presence of model discrepancy

Surrogate models

Local sensitivity analysis

Global sensitivity analysis

Appendix a. Concepts from functional analysis

Bibliography

Index.

Notes:

Includes bibliographical references and index.

Title from title screen, viewed 09/28/2013.

ISBN:

9781611973228

161197321X

OCLC:

859271907

Publisher Number:

CS12 SIAM