| KEYWORD |
Hybrid data-driven models for magnetic power loss prediction in power electronics applications
Reference persons LUIGI SOLIMENE
Description This thesis activity aims to develop, compare, and validate different models of magnetic material losses in the context of power electronics applications. Specifically, the goal is to combine the use of modern machine learning techniques with more traditional physical modeling of loss phenomena in magnetic materials in the power conversion field. These models will be developed on measured loss data under different operating conditions typical of power electronics applications.
Deadline 14/02/2025
PROPONI LA TUA CANDIDATURA