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Multidisciplinary System Modeling and Optimization Read chapter Read first chapter

2020 | OriginalPaper | Chapter

8. Multi-Fidelity for MDO Using Gaussian Processes

Authors : Nicolas Garland, Rodolphe Le Riche, Yann Richet, Nicolas Durrande

Published in: Aerospace System Analysis and Optimization in Uncertainty

Publisher: Springer International Publishing

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Abstract

The challenges of handling uncertainties within an MDO process have been discussed in Chapters 6 and 7. Related concepts to multi-fidelity are introduced in this chapter. Indeed, high-fidelity models are used to represent the behavior of a system with an acceptable accuracy. However, these models are computationally intensive and they cannot be repeatedly evaluated, as required in MDO. Low-fidelity models are more suited to the early design phases as they are cheaper to evaluate. But they are often less accurate because of simplifications such as linearization, restrictive physical assumptions, dimensionality reduction, etc. Multi-fidelity models aim at combining models of different fidelities to achieve the desired accuracy at a lower computational cost. In Section 8.2, the connection between MDO, multi-fidelity, and cokriging is made through a review of past works and system representations of code architectures.

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previous chapter Uncertainty-Based Multidisciplinary Design Optimization (UMDO)
next chapter MDO Related Issues: Multi-Objective and Mixed Continuous/Discrete Optimization
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Metadata
Title
Multi-Fidelity for MDO Using Gaussian Processes
Authors
Nicolas Garland
Rodolphe Le Riche
Yann Richet
Nicolas Durrande
Copyright Year
2020
Book
Aerospace System Analysis and Optimization in Uncertainty

Print ISBN: 978-3-030-39125-6

Electronic ISBN: 978-3-030-39126-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39126-3_8