Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
Distributed, Parallel, and Cluster Computing
2022-07-11 v2 Machine Learning
Numerical Analysis
Numerical Analysis
Abstract
We propose a data-driven framework to increase the computational efficiency of the explicit finite element method in the structural analysis of soft tissue. An encoder-decoder long short-term memory deep neural network is trained based on the data produced by an explicit, distributed finite element solver. We leverage this network to predict synchronized displacements at shared nodes, minimizing the amount of communication between processors. We perform extensive numerical experiments to quantify the accuracy and stability of the proposed synchronization-avoiding algorithm.
Cite
@article{arxiv.2207.02194,
title = {Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue},
author = {Guoxiang Grayson Tong and Daniele E. Schiavazzi},
journal= {arXiv preprint arXiv:2207.02194},
year = {2022}
}