The use of deep neural network (DNN) models as surrogates for linear and nonlinear structural dynamical systems is explored. The goal is to develop DNN based surrogates to predict structural response, i.e., displacements and accelerations, for given input (harmonic) excitations. In particular, the focus is on the development of efficient network architectures using fully-connected, sparsely-connected, and convolutional network layers, and on the corresponding training strategies that can provide a balance between the overall network complexity and prediction accuracy in the target dataspaces. For linear dynamics, sparsity patterns of the weight matrix in the network layers are used to construct convolutional DNNs with sparse layers. For nonlinear dynamics, it is shown that sparsity in network layers is lost, and efficient DNNs architectures with fully-connected and convolutional network layers are explored. A transfer learning strategy is also introduced to successfully train the proposed DNNs, and various loading factors that influence the network architectures are studied. It is shown that the proposed DNNs can be used as effective and accurate surrogates for predicting linear and nonlinear dynamical responses under harmonic loadings.

翻译：探索了深海神经网络模型(DNN)作为线性和非线性结构动态系统的代孕,目的是开发基于 DNN 的代孕模型,以预测结构反应,即偏移和加速,用于给定输入(和谐)振动,特别是侧重于开发高效网络结构,利用完全连接的、分散连接的和动态网络层,以及相应的培训战略,在目标数据空间的总体网络复杂性和预测准确性之间提供平衡。对于线性动态,网络层重量矩阵的宽度模式用于构建具有稀薄层的共振 DNNS。对于非线性动态,显示网络层的宽度已经消失,探索具有完全连接和动态网络层的高效 DNNS 结构。还引入了转移学习战略,以成功培训拟议的DNP,并研究影响网络结构的各种装载因素。据显示,拟议的DNNP可用作在线性和非线性承载性动态反应下的有效和准确的轴承载。