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Optimization under uncertainty with ...

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Optimization under uncertainty with applications to aerospace engineering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimization under uncertainty with applications to aerospace engineeringedited by Massimiliano Vasile.
其他作者:	Vasile, Massimiliano.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	vi, 573 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mathematical optimization.
電子資源:	https://doi.org/10.1007/978-3-030-60166-9
ISBN:	9783030601669$q(electronic bk.)

Optimization under uncertainty with applications to aerospace engineering
Optimization under uncertainty with applications to aerospace engineering[electronic resource] /edited by Massimiliano Vasile. - Cham :Springer International Publishing :2021. - vi, 573 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9783030601669$q(electronic bk.)

Standard No.: 10.1007/978-3-030-60166-9doiSubjects--Topical Terms:

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

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520 $a 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.
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