  This paper presents an adaptive convolutional neural network (CNN)
architecture that can automate diverse topology optimization (TO) problems
having different underlying physics. The architecture uses the encoder-decoder
networks with dense layers in the middle which includes an additional adaptive
layer to capture complex geometrical features. The network is trained using the
dataset obtained from the three open-source TO codes involving different
physics. The robustness and success of the presented adaptive CNN are
demonstrated on compliance minimization problems with constant and
design-dependent loads and material bulk modulus optimization. The architecture
takes the user's input of the volume fraction. It instantly generates optimized
designs resembling their counterparts obtained via open-source TO codes with
negligible performance and volume fraction error.
