3D Design Using Generative Adversarial Networks and Physics-Based Validation

Dule Shu, James Cunningham, Gary Stump, Simon W. Miller, Michael A. Yukish, Timothy W. Simpson, Conrad S. Tucker

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


The authors present a generative adversarial network (GAN) model that demonstrates how to generate 3D models in their native format so that they can be either evaluated using complex simulation environments or realized using methods such as additive manufacturing. Once initially trained, the GAN can create additional training data itself by generating new designs, evaluating them in a physics-based virtual environment, and adding the high performing ones to the training set. A case study involving a GAN model that is initially trained on 4045 3D aircraft models is used for demonstration, where a training data set that has been updated with GAN-generated and evaluated designs results in enhanced model generation, in both the geometric feasibility and performance of the designs. Z-tests on the performance scores of the generated aircraft models indicate a statistically significant improvement in the functionality of the generated models after three iterations of the training-evaluation process. In the case study, a number of techniques are explored to structure the generate-evaluate process in order to balance the need to generate feasible designs with the need for innovative designs.

Original languageEnglish (US)
Article number071701
JournalJournal of Mechanical Design
Issue number7
StatePublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of '3D Design Using Generative Adversarial Networks and Physics-Based Validation'. Together they form a unique fingerprint.

Cite this