DATED: Guidelines for Creating Synthetic Datasets for Engineering Design
  Applications
Cyril Picard Jürg Schiffmann Faez Ahmed
Exploiting the recent advancements in artificial intelligence, showcased by
ChatGPT and DALL-E, in real-world applications necessitates vast,
domain-specific, and publicly accessible datasets. Unfortunately, the scarcity
of such datasets poses a significant challenge for researchers aiming to apply
these breakthroughs in engineering design. Synthetic datasets emerge as a
viable alternative. However, practitioners are often uncertain about generating
high-quality datasets that accurately represent real-world data and are
suitable for the intended downstream applications. This study aims to fill this
knowledge gap by proposing comprehensive guidelines for generating, annotating,
and validating synthetic datasets. The trade-offs and methods associated with
each of these aspects are elaborated upon. Further, the practical implications
of these guidelines are illustrated through the creation of a turbo-compressors
dataset. The study underscores the importance of thoughtful sampling methods to
ensure the appropriate size, diversity, utility, and realism of a dataset. It
also highlights that design diversity does not equate to performance diversity
or realism. By employing test sets that represent uniform, real, or
task-specific samples, the influence of sample size and sampling strategy is
scrutinized. Overall, this paper offers valuable insights for researchers
intending to create and publish synthetic datasets for engineering design,
thereby paving the way for more effective applications of AI advancements in
the field. The code and data for the dataset and methods are made publicly
accessible at https://github.com/cyrilpic/radcomp .