Elastic metamaterial design based on deep learning and gradient optimization
A novel design method based on deep learning and gradient optimization was proposed to establish a flexible and general framework for fast iterative design of elastic metamaterials and achieve simultaneous optimization of topology structure and material considering material discretization.The design network composed of variational autoencoders and band gap neural network was developed as the framework,and auto-differentiation techniques and gradient optimization algorithms were employed to iteratively tune the design variables with the gradient information.Furthermore,a co-optimization strategy was further proposed to consider the material discretization,so that the structure was optimized while the optimal material was selected from the material depot.Band gap width maximization under constraints and on-demand design were carried out respectively,and the effects of simultaneous optimization and topological configuration were explored.Results showed that the simultaneous optimization provided superior performance compared to separate optimization of materials and topology structures.Additionally,the multilayer configuration can achieve basic units with smaller sizes under the same objectives and material composition.Furthermore,the numerical simulation results of frequency and time domain analyses showed that the designed elastic metamaterials exhibited significant vibration damping performance in the target band gap range.
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