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Deep generative models have demonstrated effectiveness in learning compactand expressive design representations that significantly improve geometricdesign optimization. However, these models do not consider the uncertaintyintroduced by manufacturing or fabrication. Past work that quantifies suchuncertainty often makes simplified assumptions on geometric variations, whilethe "real-world" uncertainty and its impact on design performance are difficultto quantify due to the high dimensionality. To address this issue, we propose aGenerative Adversarial Network-based Design under Uncertainty Framework(GAN-DUF), which contains a deep generative model that simultaneously learns acompact representation of nominal (ideal) designs and the conditionaldistribution of fabricated designs given any nominal design. We demonstratedthe framework on two real-world engineering design examples and showed itscapability of finding the solution that possesses better performances afterfabrication.

Deep Generative Models for Geometric Design Under Uncertainty