Enhancing Engineering Design Evaluation through Comprehensive Metrics for Deep Generative Models

In engineering design, the reliance on deep generative models (DGMs) has surged in recent years. However, evaluating these models has predominantly revolved around statistical similarity, often neglecting critical aspects such as design constraints, diversity, and novelty. As a result, the need for a more comprehensive and nuanced evaluation framework has become increasingly apparent. To address […]

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