  Typical topology optimization methods require complex iterative calculations,
which cannot meet the requirements of fast computing applications. The neural
network is studied to reduce the time of computing the optimization result,
however, the data-driven method for fluid topology optimization is less of
discussion. This paper intends to introduce a neural network architecture that
avoids time-consuming iterative processes and has a strong generalization
ability for topology optimization for Stokes flow. Different neural network
methods including Convolution Neural Networks (CNN), conditional Generative
Adversarial Networks (cGAN), and Denoising Diffusion Implicit Models (DDIM)
which have been already successfully used in solid structure optimization
problems are mutated and examined for fluid topology optimization cases. The
presented neural network method is tested on the channel flow topology
optimization problems for Stokes flow. The results have shown that our
presented method has high pixel accuracy and gains a 663 times decrease on
average in execution time compared with the conventional method.
