Comparison of uncertainty quantification process using statistical and data mining algorithms

W. Chai (1) , Alexandre Saidi (2, 3) , Abdelmalek Zine (4) , C. Droz (1) , W. You (1) , Mohamed Ichchou (1)

1 LTDS - Laboratoire de Tribologie et Dynamique des Systèmes
2 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 imagine - Extraction de Caractéristiques et Identification
4 ICJ - Institut Camille Jordan

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.

Mots clés: Global Sensitivity Analysis; Random Forest; FAST; Sound Transmission Loss; Sandwich panel; Composite material

Domaines: Acoustique [physics.class-ph]; Equations aux dérivées partielles [math.AP]; Analyse fonctionnelle [math.FA]; Analyse numérique [math.NA]; Mécanique [physics.med-ph]

W. Chai, Alexandre Saidi, Abdelmalek Zine, C. Droz, W. You, et al.. Comparison of uncertainty quantification process using statistical and data mining algorithms. Structural and Multidisciplinary Optimization, 2020, 61 (2), pp.587-598. ⟨10.1007/s00158-019-02381-w⟩. ⟨hal-02712320⟩