  2. In Section 3, we used some vague statements to affirm the training process
of the neural network, which cannot support others to reproduce the results of
the paper. In addition, this section does not show the difference between this
paper and other work, nor does it reflect innovation. 3. In Section 5, the
numerical examples are not compared with other multi-resolution methods, which
is not enough to explain the superiority of the method proposed in this paper.
Figure 10 also fails to show that this method is significantly improved
compared to the traditional method. Thanks for your understanding.
  Topology optimization is a pioneering design method that can provide various
candidates with high mechanical properties. However, the high-resolution for
the optimum structures is highly desired, normally in turn leading to
computationally intractable puzzle, especially for the famous Solid Isotropic
Material with Penalization (SIMP) method. In this paper, we introduce the
Super-Resolution Convolutional Neural Network (SRCNN) technique into topology
optimization framework to improve the resolution of topology solutions with
extremely high computational efficiency. Additionally, a pooling strategy is
established to balance the number of finite element analysis (FEA) and the
output mesh in optimization process. Considering the high training cost of 3D
neural networks, several 2D neural networks are combined to deal with 3D
topology optimization design problems. The combined treatment method used in 3D
topology optimization design eliminates the expense of retraining 3D
convolutional neural network and guarantees the quality of 3D design. Some
typical examples justify that the high-resolution topology optimization method
adopting SRCNN has excellent applicability and high efficiency.
