| Autor: |
Ming Jiang, Brian Gallagher, Alister Maguire, Keith Henderson, George F. Weinert, Noah Mandell |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
Applications of Machine Learning. |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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