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Evaluating the Quality of Finite Element Meshes with Machine Learning
This paper addresses the problem of evaluating the quality of finite element meshes for the purpose of structural mechanic simulations.
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent.
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