Aylar Partovizadeh

Aylar Partovizadeh M.Sc.

Working area(s)


work +49 6151 16-24414

Work S2|17 31
Schloßgartenstr. 8
64289 Darmstadt

Tuesday, 15:00-16:00 and by appointment (in either case, please contact me by email beforehand)

This project of TRR 361 will develop data-driven surrogate modelling methods to enable uncertainty quantification (UQ) studies within the context of electric machine design. First, material and geometrical uncertainties in the form of random fields will be stochastically modelled. Next, machine learning regression algorithms will be designed to approximate the dependence of electric machine quantities of interest on uncertain design parameters. Last, surrogate-based UQ methods for failure probability estimation and multivariate sensitivity analysis, two important and computationally demanding UQ tasks, will be developed to quantify the impact of uncertainty, provide novel insights, and facilitate improved machine designs.