Overview
- Nominated as an outstanding PhD thesis by Technische Universität Darmstadt, Germany
- Describes improved methods for quantifying uncertainties in manufacturing processes
- Combines machine learning with mathematical optimization techniques
Part of the book series: Springer Theses (Springer Theses)
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About this book
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 effort of yield estimation, and of single- and multi-objective yield optimization under uncertainty. All in all, this book presents novel strategies for quantification of uncertainty and optimization under uncertainty, with practical details to improve the design of electrotechnical devices, yet the methods can be used for any design process affected by uncertainties.
Keywords
- Robust Design Optimization
- Modeling Electromagnetic Phenomena
- Design and Optimization of Electrotechnical Devices
- Yield Optimization
- Mixed Gradient Optimization
- Maxwell’s equations
- Manufacturing Uncertainties
- Adaptive Newton-Monte Carlo method
- Uncertainty quantification
- Permanent Magnet Synchronous Machine
- Multi-objective Yield Optimization
- Gaussian Process Regression
- Hermite Least Squares Optimization
- Stochastic Collocation
- Hybrid Monte Carlo Method
- Hermite BOBYQA
Table of contents (7 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Design Methods for Reducing Failure Probabilities with Examples from Electrical Engineering
Authors: Mona Fuhrländer
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-031-37019-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-37018-2Published: 29 August 2023
Softcover ISBN: 978-3-031-37021-2Due: 29 September 2023
eBook ISBN: 978-3-031-37019-9Published: 28 August 2023
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XXII, 153
Number of Illustrations: 11 b/w illustrations, 30 illustrations in colour
Topics: Microwaves, RF and Optical Engineering, Engineering Design, Mathematical Modeling and Industrial Mathematics