Deep Generative Models in Engineering Design: A Review
release_qb2ninmcwbglpk2fdkskev2ini
by
Lyle Regenwetter, Amin Heyrani Nobari, Faez Ahmed
2021
Abstract
Automated design synthesis has the potential to revolutionize the modern
human design process and improve access to highly optimized and customized
products across countless industries. Successfully adapting generative Machine
Learning to design engineering may be the key to such automated design
synthesis and is a research subject of great importance. We present a review
and analysis of Deep Generative Learning models in engineering design. Deep
Generative Models (DGMs) typically leverage deep networks to learn from an
input dataset and learn to synthesize new designs. Recently, DGMs such as
Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs),
feedforward Neural Networks (NNs) and certain Deep Reinforcement Learning (DRL)
frameworks have shown promising results in design applications like structural
optimization, materials design, and shape synthesis. The prevalence of DGMs in
Engineering Design has skyrocketed since 2016. Anticipating continued growth,
we conduct a review of recent advances with the hope of benefitting researchers
interested in DGMs for design. We structure our review as an exposition of the
algorithms, datasets, representation methods, and applications commonly used in
the current literature. In particular, we discuss key works that have
introduced new techniques and methods in DGMs, successfully applied DGMs to a
design-related domain, or directly supported development of DGMs through
datasets or auxiliary methods. We further identify key challenges and
limitations currently seen in DGMs across design fields, such as design
creativity, handling complex constraints and objectives, and modeling both form
and functional performance simultaneously. In our discussion we identify
possible solution pathways as key areas on which to target future work.
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