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• W2971399424 abstract "The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from failures that require users to adjust a set of parameters to control mesh relaxation. In this paper, we present a deep learning framework for predicting mesh relaxation in ALE simulations. Our framework is designed to train a neural network using data generated from existing ALE simulations developed by expert users. In order to capture the spatial coherence inherent in simulations, we apply convolutional-deconvolutional neural networks to achieve up to 0.99 F1 score in predicting mesh relaxation." @default.
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• W2971399424 date "2019-09-06" @default.
• W2971399424 modified "2023-10-14" @default.
• W2971399424 title "A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations" @default.
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• W2971399424 doi "https://doi.org/10.1117/12.2529731" @default.
• W2971399424 hasPublicationYear "2019" @default.
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