Multi-Fidelity for MDO Using Gaussian Processes

Nicolas Garland, Rodolphe Riche (), Yann Richet and Nicolas Durrande
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Nicolas Garland: Ecole des Mines de Saint-Etienne
Rodolphe Riche: CNRS LIMOS at Mines Saint-Etienne
Yann Richet: Institut de Radioprotection et de Sûreté Nucléaire
Nicolas Durrande: PROWLER.io

Chapter Chapter 8 in Aerospace System Analysis and Optimization in Uncertainty, 2020, pp 295-320 from Springer

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-39126-3_8

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DOI: 10.1007/978-3-030-39126-3_8

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