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Proceedings Paper

A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations
Author(s): Ming Jiang; Brian Gallagher; Noah Mandell; Alister Maguire; Keith Henderson; George Weinert
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Paper 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.

Paper Details

Date Published: 6 September 2019
PDF: 15 pages
Proc. SPIE 11139, Applications of Machine Learning, 111390O (6 September 2019);
Show Author Affiliations
Ming Jiang, Lawrence Livermore National Lab. (United States)
Brian Gallagher, Lawrence Livermore National Lab. (United States)
Noah Mandell, Princeton Univ. (United States)
Alister Maguire, Lawrence Livermore National Lab. (United States)
Keith Henderson, Lawrence Livermore National Lab. (United States)
George Weinert, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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