A RECURRENT CONVOLUTIONAL NEURAL NETWORK SURROGATE MODEL FOR DYNAMIC MESHFREE ANALYSIS

In addition to the complexity of meshfree approximation that needs extra computational effort, the dynamic meshfree analysis requires the computation of dynamic response recursively at each time step, which significantly lowers the overall computational efficiency. In this work, the intrinsic relationships are established between meshfree discrete data and machine learning training samples, and recursive computational procedure of dynamic meshfree analysis and temporal sequence information transmission mode of recurrent convolution neural networks. With the aid of these intrinsic links, a recurrent convolutional neural network structure design method for meshfree discretization is proposed, which is then used to develop a recurrent convolution neural network surrogate model for dynamic meshfree analysis. This surrogate model takes full advantage of the flexibility of meshfree discretization. Meanwhile, meshfree analysis can provide versatile and highly accurate numerical samples, which then enhance the generality and applicability of the proposed surrogate model for dynamic meshfree analysis. Besides, the unique historical memory characteristics of the recurrent module embedded in the recurrent convolution neural network surrogate model enable an effective processing of the sequence information, and then accelerate the dynamic meshfree computational procedure with accuracy guarantee. The efficiency and accuracy of the recurrent convolution neural network surrogate model for dynamic meshfree analysis are validated through representative examples.