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

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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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Acknowledgements

This work was supported by a NSF CAREER award #1942662 (PI DES), a NSF CDS &E award #2104831 (PI DES) and used computational resources provided through the Center for Research Computing at the University of Notre Dame. The authors would like to thank Prof. Zhiliang Xu and Prof. Guosheng Fu for their comments and suggestions that contributed to improve the quality of the present manuscript. The authors would also like to thank the anonymous reviewers for their insightful comments.

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  1. Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, 102G Crowley Hall, Notre Dame, IN, 46656, USA

    Guoxiang Grayson Tong & Daniele E. Schiavazzi

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  1. Guoxiang Grayson Tong
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Tong, G.G., Schiavazzi, D.E. Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue. Comput Mech 71, 453–479 (2023). https://doi.org/10.1007/s00466-022-02248-w

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