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Titlebook: Optimization Under Uncertainty with Applications to Aerospace Engineering; Massimiliano Vasile Book 2021 Springer Nature Switzerland AG 20

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书目名称Optimization Under Uncertainty with Applications to Aerospace Engineering
编辑Massimiliano Vasile
视频videohttp://file.papertrans.cn/704/703182/703182.mp4
概述Gathers lecture notes from leading experts in the field into one authoritative resource, providing readers with an essential starting point for cutting edge research.Provides the most modern technique
图书封面Titlebook: Optimization Under Uncertainty with Applications to Aerospace Engineering;  Massimiliano Vasile Book 2021 Springer Nature Switzerland AG 20
描述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..
出版日期Book 2021
关键词Aerospace Engineering; Bayesian Techniques; Evidence Theory for Robust Optimization; Imprecise Markov C
版次1
doihttps://doi.org/10.1007/978-3-030-60166-9
isbn_softcover978-3-030-60168-3
isbn_ebook978-3-030-60166-9
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Fundamentals of Filtering,ar the focus will be on filtering problems for time-continuous state evolution equations and time-discrete observations. It will be shown that, except for very few cases, the filtering problem has no closed-form solution, which is generally infinite-dimensional. Hence, several practical algorithms to find an approximate solution are presented.
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Response Surface Methodology,g structure design optimisation problem is used to illustrate the different phases of the response surface methodology and its application to design optimisation. This example also includes the case of noisy data.
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Risk Measures in the Context of Robust and Reliability Based Optimization, will be then illustrated in an example problem of robust aerodynamic design optimization. The focus is also given to advanced techniques for error and confidence interval estimations and how they can be used in the context of robust optimization to improve the overall efficiency and effectiveness of the process.
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