EMC Professional Talk: Using Machine-Learned Models for EMC Optimization of Power Electronic Devices

EMC Professional Talk: Using Machine-Learned Models for EMC Optimization of Power Electronic Devices
Dr. Jan Hansen, Assistant Professor
Graz University of Technology & Silicon
Austria Labs, Graz, Austria

Abstract:
With increasing sophistication of power electronics design, rigid optimization becomes important. In
EMC, numerical modeling still suffers, in case of complex models and high frequencies, from long
computation times and lack of model accuracy, which prevent the desirable solution of multi-
objective optimization problems. Surrogate models may help to overcome these problems. This
presentation shows several examples of successfully trained surrogate models, indicating that
machine learning enables to evaluate complex, parameterized EMC models in millions of runs
within a single day.

For further information, pleas see event page: https://r8.ieee.org/germany-emc/wp-content/uploads/sites/146/2023/11/ieee_german_emc_chapter_professional_talk_Hansen.pdf