Comparison of uncertainty quantification process using statistical and data mining algorithms

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.