  This paper proposes a deep Convolutional Neural Network(CNN) with strong
generalization ability for structural topology optimization. The architecture
of the neural network is made up of encoding and decoding parts, which provide
down- and up-sampling operations. In addition, a popular technique, namely
U-Net, was adopted to improve the performance of the proposed neural network.
The input of the neural network is a well-designed tensor with each channel
includes different information for the problem, and the output is the layout of
the optimal structure. To train the neural network, a large dataset is
generated by a conventional topology optimization approach, i.e. SIMP. The
performance of the proposed method was evaluated by comparing its efficiency
and accuracy with SIMP on a series of typical optimization problems. Results
show that a significant reduction in computation cost was achieved with little
sacrifice on the optimality of design solutions. Furthermore, the proposed
method can intelligently solve problems under boundary conditions not being
included in the training dataset.
