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Computational Mechanics

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15-11-2022 | Original Paper

Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue

Authors: Guoxiang Grayson Tong, Daniele E. Schiavazzi

Published in: Computational Mechanics | Issue 3/2023

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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.

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Title: Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
Authors: Guoxiang Grayson Tong
Daniele E. Schiavazzi
Publication date: 15-11-2022
Publisher: Springer Berlin Heidelberg
Published in: Computational Mechanics / Issue 3/2023
Print ISSN: 0178-7675
Electronic ISSN: 1432-0924
DOI: https://doi.org/10.1007/s00466-022-02248-w

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