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

arXiv: https://arxiv.org/abs/2207.02194 · HTML: https://mlsanity.com/paper/2207.02194/

## 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 (most cited first, 25 of 25)

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- [SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics](https://mlsanity.com/paper/2004.02322/) (2020)
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- [Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders](https://mlsanity.com/paper/2201.05136/) (2022)
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- [Data-Driven Deep Learning of Partial Differential Equations in Modal Space](https://mlsanity.com/paper/1910.06948/) (2019)
- [Deep Neural Network Modeling of Unknown Partial Differential Equations in Nodal Space](https://mlsanity.com/paper/2106.03603/) (2021)
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- [SINDy-BVP: Sparse Identification of Nonlinear Dynamics for Boundary Value Problems](https://mlsanity.com/paper/2005.10756/) (2020)
- [Learning reduced systems via deep neural networks with memory](https://mlsanity.com/paper/2003.09451/) (2020)
- [Deep Learning of Chaotic Systems from Partially-Observed Data](https://mlsanity.com/paper/2205.08384/) (2022)
- [Neural-PDE: A RNN based neural network for solving time dependent PDEs](https://mlsanity.com/paper/2009.03892/) (2020)
- [Multifidelity data fusion in convolutional encoder/decoder networks](https://mlsanity.com/paper/2205.05187/) (2022)
- [Recurrent Neural Networks for Partially Observed Dynamical Systems](https://mlsanity.com/paper/2109.11629/) (2021)
- [An ensemble solver for segregated cardiovascular FSI](https://mlsanity.com/paper/2101.09059/) (2021)

## Built on by (most cited first, 2 of 2)

- [InVAErt networks: a data-driven framework for model synthesis and identifiability analysis](https://mlsanity.com/paper/2307.12586/) (2023)
- [Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks](https://mlsanity.com/paper/2605.04134/) (2026)

## 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.