  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.
