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Comparison of uncertainty quantification process using statistical and data mining algorithms

  • W. Chai
  • A. Saidi
  • A. Zine
  • C. DrozEmail author
  • W. You
  • M. Ichchou
Research Paper
  • 6 Downloads

Abstract

Uncertainty quantification has always been an important topic in model reduction and simulation of complex systems. In this aspect, global sensitivity analysis (GSA) methods such as Fourier amplitude sensitivity test (FAST) are well recognized as effective algorithms. Recently, some data-based metamodeler such as Random Forest (RF) also developed their own variable importance selection solutions for parameters with perturbations. This paper proposes a visual comparison of these two uncertainty quantification methods, using datasets retrieved from vibroacoustic models. Their results have a lot in common and are capable to explain many results. The remarkable agreement between methods under fundamentally different definitions can potentially improve their compatibility in various occasions.

Keywords

Global sensitivity analysis Random forest Fourier amplitude sensitivity test Sound transmission loss Sandwich panel Composite material 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Vibroacoustics & Complex Media Research Group, LTDS - CNRS UMR 5513École Centrale de LyonÉcullyFrance
  2. 2.LIRIS - CNRS UMR 5205École Centrale de LyonÉcullyFrance
  3. 3.ICJ - CNRS UMR 5208École Centrale de LyonÉcullyFrance

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