利用前馈神经网络减少有限元后处理的计算时间

M. Zlatić, M. Čanađija
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引用次数: 0

摘要

随着最近神经网络使用量的激增,机器学习库的使用和实现变得更加方便。在本文中,我们研究了使用神经网络的可能性,以便更快地处理由有限元计算得到的位移,并取代现有的后处理程序。该方法是在二维有限元上实现的,因为它们相对容易使用和操作。与传统的后处理方法相比,观察到速度加快。本文还对该方法的进一步应用进行了展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reducing computational time for FEM postprocessing through the use of feedforward neural networks
With the recent surge in neural network usage, machine learning libraries have become more convenient to use and implement. In this paper we investigate the possibility of using neural networks in order to faster process displacements obtained from finite element calculation and replace existing post-processing procedures. The method is implemented on 2D finite elements for their relative ease of usage and manipulation. A speed up is observed in comparison to traditional methods of post-processing. Possible further applications of this method are also presented in this paper.
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