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The use of deep neural network (DNN) models as surrogates for linear andnonlinear structural dynamical systems is explored. The goal is to develop DNNbased surrogates to predict structural response, i.e., displacements andaccelerations, for given input (harmonic) excitations. In particular, the focusis on the development of efficient network architectures using fully-connected,sparsely-connected, and convolutional network layers, and on the correspondingtraining strategies that can provide a balance between the overall networkcomplexity and prediction accuracy in the target dataspaces. For lineardynamics, sparsity patterns of the weight matrix in the network layers are usedto construct convolutional DNNs with sparse layers. For nonlinear dynamics, itis shown that sparsity in network layers is lost, and efficient DNNsarchitectures with fully-connected and convolutional network layers areexplored. A transfer learning strategy is also introduced to successfully trainthe proposed DNNs, and various loading factors that influence the networkarchitectures are studied. It is shown that the proposed DNNs can be used aseffective and accurate surrogates for predicting linear and nonlinear dynamicalresponses under harmonic loadings.

On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics