Uncertainty in Engineering : Introduction to Methods and Applications

Louis J. M. Aslett

ISBN 10: 3030836398 / ISBN 13: 9783030836399

Published by Springer International Publishing, Springer International Publishing, 2021

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This open accessbook provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace ight modelling. Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.

Seller Inventory # 9783030836399

Bibliographic Details

• Title: Uncertainty in Engineering : Introduction to Methods and Applications
• Publisher: Springer International Publishing, Springer International Publishing
• Publication Date: 2021
• Language: English
• ISBN 10: 3030836398
• ISBN 13: 9783030836399
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 9D millimeters 235 millimeters width by 155 millimeters height by 9 millimeters depth
• Weight: 248 grams

About this title

Synopsis

This open access book provides an introduction to uncertainty quantification in engineering. Starting with preliminaries on Bayesian statistics and Monte Carlo methods, followed by material on imprecise probabilities, it then focuses on reliability theory and simulation methods for complex systems. The final two chapters discuss various aspects of aerospace engineering, considering stochastic model updating from an imprecise Bayesian perspective, and uncertainty quantification for aerospace flight modelling.

Written by experts in the subject, and based on lectures given at the Second Training School of the European Research and Training Network UTOPIAE (Uncertainty Treatment and Optimization in Aerospace Engineering), which took place at Durham University (United Kingdom) from 2 to 6 July 2018, the book offers an essential resource for students as well as scientists and practitioners.

About the Author

Louis Aslett is an Associate Professor in the Department of Mathematical Sciences at Durham University.  His main research interests span applied work in statistical machine learning and computational statistics, as well as methodological work in reliability theory and at the interface between cryptography and statistics. Threaded through this work is an interest in developing these statistical methods so that they are amenable to implementation in modern high performance computing architectures.

Frank Coolen is a Professor in the Department of Mathematical Sciences at Durham University. His main research activities are in the theory and methods of statistics and reliability. He has developed methods in nonparametric predictive inference, a frequentist statistics methodology based on few assumptions made possible through the use of imprecise probability for uncertainty quantification. He has presented the survival signature as a powerful tool for system reliability.He has been on the editorial boards of several journals, including the Journal of Statistical Theory and Practice, Communications in Statistics, and the Journal of Risk and Reliability. 

Jasper De Bock is an Assistant Professor in the Department of Electronics and Information Systems at Ghent University. He is also the current president of SIPTA  (the Society for Imprecise Probabilities: Theories and Applications). His research is concerned with the foundations of imprecise probabilities and their application to robust inference and decision making, with a particular focus on imprecise stochastic processes and choice functions.