| 書目名稱 | Design Methods for Reducing Failure Probabilities with Examples from Electrical Engineering | | 編輯 | Mona Fuhrl?nder | | 視頻video | http://file.papertrans.cn/269/268403/268403.mp4 | | 概述 | Nominated as an outstanding PhD thesis by Technische Universit?t Darmstadt, Germany.Describes improved methods for quantifying uncertainties in manufacturing processes.Combines machine learning with m | | 叢書名稱 | Springer Theses | | 圖書封面 |  | | 描述 | .This book deals with efficient estimation and optimization methods to improve the design of electrotechnical devices under uncertainty. Uncertainties caused by manufacturing imperfections, natural material variations, or unpredictable environmental influences, may lead, in turn, to?deviations in operation. This book describes two novel methods for yield (or failure probability) estimation. Both are hybrid methods that combine the accuracy of Monte Carlo with the efficiency of surrogate models. The SC-Hybrid approach uses stochastic collocation and adjoint error indicators. The non-intrusive GPR-Hybrid approach consists of a Gaussian process regression that allows surrogate model updates on the fly. Furthermore, the book proposes an adaptive Newton-Monte-Carlo (Newton-MC) method for efficient yield optimization. In turn, to solve optimization problems with mixed gradient information, two novel Hermite-type optimization methods are described. All the proposed methods have been numerically evaluated on two benchmark problems, such as a rectangular waveguide and a permanent magnet synchronous machine. Results showed that the new methods can significantly?reduce the computational effor | | 出版日期 | Book 2023 | | 關(guān)鍵詞 | Robust Design Optimization; Modeling Electromagnetic Phenomena; Design and Optimization of Electrotech | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-37019-9 | | isbn_softcover | 978-3-031-37021-2 | | isbn_ebook | 978-3-031-37019-9Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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