| description abstract | A number of machine learning methods have been recently proposed to circumvent the high computational cost of the gradient-based topology optimization solvers. By and large, these methods show tight generalizability to unseen boundary and external loading conditions, require prohibitively large datasets for training, and do not take into consideration topological constraints of the predictions, which results in solutions with unpredictable connectivity. To address these limitations, we propose a design exploration framework for topology optimization that exploits the knowledge transfer capability of the transfer learning methods and the generative power of conditional generative adversarial networks (GANs). We show that the proposed framework significantly exceeds the generalization ability of current methods. Moreover, the proposed architecture is capable of reusing the knowledge learned on low-resolution and computationally inexpensive samples, which notably reduces both the size of the required high-resolution training datasets and the demand on the computational infrastructure needed to generate the training data. Finally, we propose and evaluate novel approaches to improve the structural connectivity of the predicted optimal topology by including topological metrics into the loss function. We show that by including the bottleneck distance between the persistence diagrams of the predicted and ground truth structures, we significantly improve the connectivity of the prediction. Together, our results reveal the ability of generative adversarial networks implemented in a transfer learning environment to serve as powerful and practical real-time design exploration tools in topology optimization. | |
