论文标题
软组织的显式分布式结构分析中，数据驱动的避免同步算法
Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue
论文作者
论文摘要
我们提出了一个数据驱动的框架，以提高软组织结构分析中显式有限元方法的计算效率。编码器 - 码头长的短期记忆深神经网络是根据明确的，分布式有限元求解器产生的数据训练的。我们利用该网络预测共享节点处的同步位移，从而最大程度地减少处理器之间的通信量。我们执行广泛的数值实验，以量化提出的避免同步算法的准确性和稳定性。
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.
