Generative design is an increasingly important tool in the industrial world. It allows the designers and engineers to easily explore vast ranges of design options, providing a cheaper and faster alternative to the trial and failure approaches. Thanks to the flexibility they offer, Deep Generative Models are gaining popularity amongst Generative Design technologies. However, developing and evaluating these models can be challenging. The field lacks accessible benchmarks, in order to evaluate and compare objectively different Deep Generative Models architectures. Moreover, vanilla Deep Generative Models appear to be unable to accurately generate multi-components industrial systems that are controlled by latent design constraints. To address these challenges, we propose an industry-inspired use case that incorporates actual industrial system characteristics. This use case can be quickly generated and used as a benchmark. We propose a Meta-VAE capable of producing multi-component industrial systems and showcase its application on the proposed use case.

翻译：生成设计是工业领域中日益重要的工具，它使设计师和工程师能够轻松探索广泛的设计选项，为试错方法提供了更便宜、更快速的替代方案。凭借其提供的灵活性，深度生成模型在生成设计技术中越来越受欢迎。然而，开发和评估这些模型可能具有挑战性。该领域缺乏可访问的基准，无法客观评估和比较不同的深度生成模型架构。此外，原始的深度生成模型似乎无法准确生成受潜在设计约束控制的多组件工业系统。为了解决这些挑战，我们提出了一个受工业启发的用例，该用例融入了实际工业系统的特性。这个用例可以快速生成并用作基准。我们提出了一种能够生成多组件工业系统的元变分自编码器，并在所提出的用例上展示了其应用。