A deep Convolutional Neural Network for topology optimization with strong generalization ability

A variety of numerical methods have sprung up later, including SIMP (Bendse, 1989; Zhou and Rozvany, 1991; Rozvany et al., 1992), evolutionary approaches(Xie and Steven, 1993), level-set method (Wang et al., 2003; Allaire et al., 2004), moving morphable components (Guo et al., 2014), and others. However, the computational cost is still one of the main hinders to widely introduce them into design practices, in particular for large structures (Sigmund and Maute, 2013). Withthe recent boost of machine learning algorithms andadvances in graphics processing units (GPU), machine learning (ML), especially the deep learning, which has been seen to make many successful stories in various fields, including automatic drive, image recognition, naturallanguage processing, and even art, may shed light on accelerating the adoption of topology optimization inmore design practices. Recently, a few attempts have been seen to apply ML on topology optimizations (Leiet al., 2018; Sosnovik and Oseledets, 2017; Banga et al., 2018; Yu et al., 2018). Theoretically, theoptimal layout of the material is a complicated function of the initial conditions based on the optimization objectiveand constraints. The neural network can implement approximating nonlinear functions by arbitrary accuracyas its depth increases. This characteristic makes it possible for the neural network to learn a target function which can directly give us the optimal structure without any iteration and effectively reduce computational time. Sosnovik and Oseledets (2017) first introduced the deep learning model to topology optimization and improved theefficiency of the optimization process by stating the problem as an image segmentation task.