Optimization under uncertainty with applications to aerospace engineering /

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Bibliographic Details
Imprint:	Cham, Switzerland : Springer, [2021]
Description:	1 online resource (vi, 573 pages) : illustrations (some color)
Language:	English
Subject:	Mathematical optimization. Measurement uncertainty (Statistics) Aerospace engineering -- Mathematics. Mathematical optimization. Measurement uncertainty (Statistics) Electronic books.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/12611654

Hidden Bibliographic Details
Other authors / contributors:	Vasile, Massimiliano, editor.
ISBN:	9783030601669 3030601668 9783030601652 303060165X 9783030601676 3030601676 9783030601683 3030601684
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. Access restricted to registered UOB users with valid accounts. Online resource; title from PDF title page (SpringerLink, viewed April 2, 2021).
Summary:	In an expanding world with limited resources, optimization and uncertainty quantification have become a necessity when handling complex systems and processes. This book provides the foundational material necessary for those who wish to embark on advanced research at the limits of computability, collecting together lecture material from leading experts across the topics of optimization, uncertainty quantification and aerospace engineering. The aerospace sector in particular has stringent performance requirements on highly complex systems, for which solutions are expected to be optimal and reliable at the same time. The text covers a wide range of techniques and methods, from polynomial chaos expansions for uncertainty quantification to Bayesian and Imprecise Probability theories, and from Markov chains to surrogate models based on Gaussian processes. The book will serve as a valuable tool for practitioners, researchers and PhD students.
Other form:	Printed edition: 9783030601652 Printed edition: 9783030601676 Printed edition: 9783030601683
Standard no.:	10.1007/978-3-030-60166-9

Table of Contents:

Introduction to Spectral Methods for Uncertainty Quantification
Introduction to Imprecise Probabilities
Uncertainty Quantification in Lasso-Type Regularization Problems
Reliability Theory
An Introduction to Imprecise Markov Chains
Fundamentals of Filtering
Introduction to Optimisation
An Introduction to Many-Objective Evolutionary Optimization
Multilevel Optimisation
Sequential Parameter Optimization for Mixed-Discrete Problems
Parameter Control in Evolutionary Optimisation
Response Surface Methodology
Risk Measures in the Context of Robust and Reliability Based Optimization
Best Practices for Surrogate Based Uncertainty Quantification in Aerodynamics and Application to Robust Shape Optimization
In-flight Icing: Modeling, Prediction, and Uncertainty
Uncertainty Treatment Applications: High-Enthalpy Flow Ground Testing
Introduction to Evidence-Based Robust Optimisation.

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