@inproceedings{ec069e25be244387a05320e5a4d0a2f2,
title = "A deep learning framework for mesh relaxation in arbitrary Lagrangian-Eulerian simulations",
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.",
keywords = "Arbitrary Lagrangian-Eulerian simulations, Deep learning, Visualization images",
author = "Ming Jiang and Brian Gallagher and Noah Mandell and Alister Maguire and Keith Henderson and George Weinert",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Applications of Machine Learning 2019 ; Conference date: 13-08-2019 Through 14-08-2019",
year = "2019",
doi = "10.1117/12.2529731",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Zelinski, \{Michael E.\} and Taha, \{Tarek M.\} and Jonathan Howe and Awwal, \{Abdul A. S.\} and Iftekharuddin, \{Khan M.\}",
booktitle = "Applications of Machine Learning",
address = "United States",
}