Neural Networks and Imbalanced Learning for Data-Driven Scientific Computing With Uncertainties
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DOI: 10.1109/access.2021.3052680
Abstract: Uncertainty quantification in complex engineering problems is challenging because of necessitating large numbers of expensive model evaluations. This paper proposes a two-stage framework for developing accurate machine learning-based surrogate models in structural engineering. The studied… read more here.
Keywords: data driven; imbalanced learning; networks imbalanced; neural networks ... See more keywords