Data-driven Metamodels for Failure Analysis of Power Electronic Modules
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This presentation explores the use of machine learning-based metamodels to efficiently assess the reliability of power electronic modules, capitalizing on their fast inference speed and high predictive power. A major focus of the presentation is the thorough analysis of often-overlooked statistical properties that are crucial for evaluating model behavior, as well as presenting a novel simulation selection technique based on active learning.