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This document provides a review of deep generative machine learning models in engineering design. It discusses how these models have the potential to revolutionize the design process through automated design synthesis. The review focuses on recent advances in deep generative models like GANs, VAEs, and reinforcement learning for applications in structural optimization, materials design, and shape synthesis. It identifies key challenges for these models in engineering design, such as modeling design performa

Author

Muhammad Umair

Language

EN