TY  - EJOU
AU  - Zeng, Qingrong 
AU  - Liu, Xiaochen 
AU  - Zhu, Xuefeng 
AU  - Zhang, Xiangkui 
AU  - Hu, Ping 

TI  - Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 141
IS  - 3
SN  - 1526-1506

AB  - Traditional topology optimization methods often suffer from the “dimension curse” problem, wherein the computation time increases exponentially with the degrees of freedom in the background grid. Overcoming this challenge, we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty (CGAN-GP). This innovative method allows for nearly instantaneous prediction of optimized structures. Given a specific boundary condition, the network can produce a unique optimized structure in a one-to-one manner. The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization (SIMP) method. Subsequently, we design a conditional generative adversarial network and train it to generate optimized structures. To further enhance the quality of the optimized structures produced by CGAN-GP, we incorporate Pix2pixGAN. This augmentation results in sharper topologies, yielding structures with enhanced clarity, de-blurring, and edge smoothing. Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms, all while maintaining an impressive accuracy rate of up to 85%, as demonstrated through numerical examples.
KW  - Real-time topology optimization; conditional generative adversarial networks; dimension curse

DO  - 10.32604/cmes.2024.052620