Computational Acceleration of Topology Optimization Using Deep Learning

Topology optimization is a computationally expensive process, especially when complicated designs are studied, and this is mainly due to its finite element analysis and iterative solvers incorporated into the algorithm. In the current work, we investigated the application of deep learning methods to computationally accelerate topology optimization. We tested and comparatively analyzed three types of improved neural network models using three different structured datasets and achieved satisfactory results that allowed for the generation of topology optimized structures in 2D and 3D domains. The results of the studies show that the improved Res-U-Net and U-Net are reliable and effective methods among deep learning approaches for the computational acceleration of topology optimization problems. Moreover, based on the results, it is evaluated that Res-U-Net gives better results than U-Net for higher iterations. We also showed that the proposed CNN method is highly accurate and required much less training time compared to existing methods.