Verification, Validation, and Uncertainty Quantification in Scientific Computing

William L. Oberkampf

ISBN 10: 131651613X / ISBN 13: 9781316516133

Published by Cambridge University Press, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Can you trust results from modeling and simulation This text provides a framework for assessing the reliability of and uncertainty included in the results used by decision makers and policy makers in industry and government. The emphasis is on models described by PDEs and their numerical solution. Procedures and results from all aspects of verification and validation are integrated with modern methods in uncertainty quantification and stochastic simulation. Methods for combining numerical approximation errors, uncertainty in model input parameters, and model form uncertainty are presented in order to estimate the uncertain response of a system in the presence of stochastic inputs and lack of knowledge uncertainty. This new edition has been extensively updated, including a fresh look at model accuracy assessment and the responsibilities of management for modeling and simulation activities. Extra homework problems and worked examples have been added to each chapter, suitable for course use or self-study.

Seller Inventory # 9781316516133

Bibliographic Details

• Title: Verification, Validation, and Uncertainty Quantification in Scientific Computing
• Publisher: Cambridge University Press
• Publication Date: 2025
• Language: English
• ISBN 10: 131651613X
• ISBN 13: 9781316516133
• Binding: Buch
• Edition: 2nd Edition
• Condition: Neu

About this title

Synopsis

Can you trust results from modeling and simulation? This text provides a framework for assessing the reliability of and uncertainty included in the results used by decision makers and policy makers in industry and government. The emphasis is on models described by PDEs and their numerical solution. Procedures and results from all aspects of verification and validation are integrated with modern methods in uncertainty quantification and stochastic simulation. Methods for combining numerical approximation errors, uncertainty in model input parameters, and model form uncertainty are presented in order to estimate the uncertain response of a system in the presence of stochastic inputs and lack of knowledge uncertainty. This new edition has been extensively updated, including a fresh look at model accuracy assessment and the responsibilities of management for modeling and simulation activities. Extra homework problems and worked examples have been added to each chapter, suitable for course use or self-study.

About the Authors

William L. Oberkampf has more than fifty years of experience in research and development in fluid dynamics, heat transfer, and solid mechanics. Over the past twnety-five years he has focused on research and teaching of verification, validation, and uncertainty quantification in modeling and simulation. He is a Fellow of AIAA and a Fellow of NAFEMS.

Christopher J. Roy is Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He has worked primarily in the area of computational fluid dynamics, but has participated in or organized multiple validation experiments. He has taught more than fifty short courses in the field of verification, validation, and uncertainty quantification and has more than 200 publications in the field.