Peri-Net: Analysis of Crack Patterns Using Deep Neural Networks
Peri-Net: Analysis of Crack Patterns Using Deep Neural Networks
复制标题
Peri-Net：使用深度神经网络分析裂纹模式
DOI：
10.1007/s42102-019-00013-x
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jeong, Wontae
中科院分区：
文献类型：
--
作者：
Kim, Moonseop;Winovich, Nick;Lin, Guang;Jeong, Wontae
在这项工作中，我们引入了卷积神经网络，旨在预测和分析由分子动力学（MD）碰撞模拟引起的磁盘上的损伤模式。所考虑的模拟是专门为在圆盘上产生裂纹而设计的，因此，需要偏导数信息的数值方法，如有限元分析，是不适用的。然而，这些模拟可以使用周期动力学进行，周期动力学是基于积分方程的经典连续介质力学的非局部扩展，克服了变形不连续体建模的困难。尽管这种非局部扩展为MD仿真提供了一个高精度的模型，但随着仿真规模的扩大，计算复杂度和运行时间也随之大大增加。我们建议使用神经网络近似来补充周围动力学模拟，提供快速估计，保持完整模拟的大部分准确性，同时将模拟次数减少1500倍。我们提出了两个不同的卷积神经网络：一个训练来执行正向问题，即在提供碰撞物体撞击位置的情况下预测磁盘上的损伤模式；另一个训练来解决逆向问题，即在给出损伤模式的情况下识别碰撞位置、角度、速度和大小。
In this work, we introduce convolutional neural networks designed to predict and analyze damage patterns on a disk resulting from molecular dynamic (MD) collision simulations. The simulations under consideration are specifically designed to produce cracks on the disk and, accordingly, numerical methods which require partial derivative information, such as finite element analysis, are not applicable. These simulations can, however, be carried out using peridynamics, a nonlocal extension of classical continuum mechanics based on integral equations which overcome the difficulties in modeling deformation discontinuities. Although this nonlocal extension provides a highly accurate model for the MD simulations, the computational complexity and corresponding run times increase greatly as the simulations grow larger. We propose the use of neural network approximations to complement peridynamic simulations by providing quick estimates which maintain much of the accuracy of the full simulations while reducing simulation times by a factor of 1500. We propose two distinct convolutional neural networks: one trained to perform theforward problemof predicting the damage pattern on a disk provided the location of a colliding object’s impact, and another trained to solve theinverse problemof identifying the collision location, angle, velocity, and size given the resulting damage pattern.
使用近场动力学理论分析钎焊单搭接接头
影响因子：
4
作者：
Bahattin Kilic;Bahattin Kilic;E. Madenci;D. Ambur;D. Ambur
通讯作者：
D. Ambur
近场动力学方法在粘弹性材料行为预测中的应用
影响因子：
3.3
作者：
N. Nikabdullah;M. .. Azizi;R. Alebrahim;S. Singh;A. K. Elwaleed
通讯作者：
A. K. Elwaleed