This article is the first attempt to employ deep learning to estimate the frequency performance of the rotating multi-layer nanodisks. The optimum values of the parameters involved in the mechanism of the fully connected neural network are determined through the momentum-based optimizer. The strength of the method applied in this survey comes from the high accuracy besides lower epochs needed to train the multi-layered network. It should be mentioned that the current nanostructure is modeled as a nanodisk on the viscoelastic substrate. Due to rotation, the centrifugal and Coriolis effects are considered. Hamilton’s principle and generalized differential quadrature method (GDQM) are presented for obtaining and solving the governing equations of the high-speed rotating nanodisk on a viscoelastic substrate. The outcomes show that the number of layers viscoelastic foundation, angular velocity speed, angle of ply, nonlocal, and length-scale parameters have a considerable impact on the amplitude and vibration behavior of a laminated rotating cantilevered nanodisk. As an applicable result in related industries, in the initial value of radius ratio, damping of the foundation does not have any effect on the dynamics of the system, but when the outer radius is bigger enough, the effect of damping parameter on the frequency of the laminated nanostructure will be bold sharply.

本文是首次尝试使用深度学习来估计旋转多层纳米盘的频率性能。全连接神经网络机制中涉及的参数的最佳值是通过基于动量的优化器确定的。本次调查中应用的方法的优势在于除了训练多层网络所需的较低时期之外还具有较高的准确性。应该提到的是，当前的纳米结构被建模为粘弹性基底上的纳米盘。由于旋转，考虑了离心效应和科里奥利效应。提出了 Hamilton 原理和广义微分正交法 (GDQM)，用于获得和求解粘弹性基底上高速旋转纳米盘的控制方程。结果表明，粘弹性基础层数、角速度、铺层角度、非局部和长度尺度参数对层压旋转悬臂纳米盘的振幅和振动行为有相当大的影响。作为相关行业的一个适用结果，在半径比初值下，基础阻尼对系统动力学没有任何影响，但当外半径足够大时，阻尼参数对系统动力学的影响层压纳米结构将是大胆的。