A Meta-Generation framework for Industrial System Generation
Fouad Oubari Raphael Meunier Rodrigue Décatoire Mathilde Mougeot
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.