mlsanityData to 14 Oct 2026

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

arXiv 2207.02194 ↗First posted 5 Jul 2022cs.DC cs.LG cs.NAcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 25 of its 60 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

25 of 60 references.

References · 25

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13 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023InVAErt networks: a data-driven framework for model synthesis and identifiability analysiscited by 6
  2. 2026Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networkscited by 0