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Multi-Fidelity for MDO Using Gaussian Processes

In: Aerospace System Analysis and Optimization in Uncertainty

Author

Listed:
  • Nicolas Garland

    (Ecole des Mines de Saint-Etienne
    CNRS LIMOS at Mines Saint-Etienne
    Institut de Radioprotection et de Sûreté Nucléaire)

  • Rodolphe Riche

    (CNRS LIMOS at Mines Saint-Etienne)

  • Yann Richet

    (Institut de Radioprotection et de Sûreté Nucléaire)

  • Nicolas Durrande

    (PROWLER.io)

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.

Suggested Citation

  • Nicolas Garland & Rodolphe Riche & Yann Richet & Nicolas Durrande, 2020. "Multi-Fidelity for MDO Using Gaussian Processes," Springer Optimization and Its Applications, in: Aerospace System Analysis and Optimization in Uncertainty, chapter 0, pages 295-320, Springer.
  • Handle: RePEc:spr:spochp:978-3-030-39126-3_8
    DOI: 10.1007/978-3-030-39126-3_8
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