A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations

Ming Jiang; Brian Gallagher; Noah Mandell; Alister Maguire; Keith Henderson; George Weinert

doi:10.1117/12.2529731

6 September 2019 A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations

Ming Jiang, Brian Gallagher, Noah Mandell, Alister Maguire, Keith Henderson, George Weinert

Proceedings Volume 11139, Applications of Machine Learning; 111390O (2019) https://doi.org/10.1117/12.2529731
Event: SPIE Optical Engineering + Applications, 2019, San Diego, California, United States

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.

Citation Download Citation

Ming Jiang, Brian Gallagher, Noah Mandell, Alister Maguire, Keith Henderson, and George Weinert "A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations", Proc. SPIE 11139, Applications of Machine Learning, 111390O (6 September 2019); https://doi.org/10.1117/12.2529731

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