Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design

Understand

Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural language processing, and drug discovery.

• However, when applied to engineering design problems, evaluating the performance of these models can be challenging, as traditional statistical metrics based on likelihood may not fully capture the requirements of engineering applications.
• This paper doubles as a review and practical guide to evaluation metrics for deep generative models (DGMs) in engineering design.
• We first summarize the well-accepted `classic' evaluation metrics for deep generative models grounded in machine learning theory.