Structural displacement field prediction method based on deep learning
Based on the basic equations of finite element method,the feature extension layer is embedded into the deep learning model,and a training set generator is developed using Abaqus software interface to achieve single model prediction of the full displacement component of the structure.Using the Keras API under the TensorFlow framework to train a deep learning model for spatial thin shell structures,a quantitative analysis of the prediction performance is conducted.The results show that:the computational efficiency of the deep learning model is significantly improved compared to the simulation model,and the prediction of the maximum displacement and distribution pattern is basically consistent with the simulation results,however there is an increase in error at the 0 displacement boundary.
deep learningmodel trainingdisplacement fieldpredictionspatial thin shell structure
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维普
