High-Fidelity FEA Simulation through Linking with Experiment Data: A Neural Network Methodology
نویسنده
چکیده
This paper proposes a novel computational framework for improving realism and fidelity of finite element analysis simulations through experimental test data. The proposed scheme utilizes an artificial neural network to learn and compensate for the differences between a finite element analysis model simulation and corresponding experiment. The proposed computational methodology is poised to significantly enhance the fidelity and realism of FEA model simulations beyond what is achievable through model updating.
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