Non Linear Dimension Reduction of Dynamic Model Output
Gerrer Claire-Eleuthèriane, Stéphane Girard · Linköping electronic conference proceedings · 2019
Most advanced mathematical methods for the analysis of numerical model cannot cope with functional outputs of dynamic Modelica models.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 illustrate on a case study how the non linear method of autoassociative model overcomes this shortcoming and provides physically interpretable data representations.