Overcoming the Obstacle of Time-dependent Model Output for Statistical Analysis by Nonlinear Methods

Abstract

Modelica models represent static or dynamic systems. Their outputs can be scalar (numbers) or time-dependent (time series). Most advanced mathematical methods for the analysis of numerical models cannot cope with functional outputs. This paper aims at showing an efficient method to reduce a time-dependent output to a few numbers. The Principal component analysis is a well-established method for dimension reduction and can be used to tackle this issue. It relies however on a linear hypothesis that limits its applicability. We adapt and implement an existing method called the auto-associative model, invented by Stéphane Girard, to overcome this shortcoming. The auto-associative model generalizes PCA, as it projects the data on a nonlinear (instead of linear) basis. It also provides physically interpretable data representations. The difference in efficiency between both methods is illustrated in a case study, the well-known bouncing ball model. We perform output reduction and reconstruction using both methods to compare the completeness of information kept throughout the dimension reduction process. Doi: 10.28991/HIJ-2021-02-01-01 Full Text: PDF

Keywords

• dimension reduction
• functional data analysis
• fmi
• otfmi
• principal component analysis
• auto-associative model
• sensitivity analysis.