AI surrogate models for lifetime prediction in power electronic modules
Published in Microelectronics Reliability, 2026
This work presents an accessible blueprint for applying AI to power electronics, utilizing machine learning metamodels to replace computationally expensive finite element simulations for lifetime prediction. We expand upon this framework by introducing a novel active learning data selection technique to further minimize simulation time, alongside rigorous residual and learning behavior analyses to ensure robust model evaluation.