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RISDDE-GAN: Integrating a Data-Driven Design Evaluator Into Generative Adversarial Networks for Desirable and Diverse Concept Generation
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Generative adversarial networks (GANs) have shown remarkable success in various generative design tasks, from topology optimization to material design, and shape parametrization. However, most generative design approaches ...